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Table of Contents
ORIGINAL ARTICLE
Year : 2019  |  Volume : 62  |  Issue : 5  |  Page : 217-225

Association between adiposity and fasting serum levels of appetite-regulating peptides: Leptin, neuropeptide Y, desacyl ghrelin, peptide YY(1-36), obestatin, cocaine and amphetamine-regulated transcript, and agouti-related protein in nonobese participants


Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Łódź, Poland

Date of Submission05-Apr-2019
Date of Decision23-Aug-2019
Date of Acceptance23-Sep-2019
Date of Web Publication24-Oct-2019

Correspondence Address:
Dr. Adam Wysokiński
Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Czechosłowacka 8/10, 92-216 Łódź
Poland
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/CJP.CJP_29_19

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  Abstract 

The objective of this study was to evaluate the association between adiposity parameters and fasting serum levels of appetite-regulating peptides: leptin, neuropeptide Y (NPY), desacyl ghrelin, peptide YY(1-36), obestatin, cocaine- and amphetamine-regulated transcript (CART), and agouti-related protein in 30 healthy, non-obese subjects. Thirty European Caucasian adult participants were included in the study (17 men and 13 women). Body composition (body fat and lean body mass) was determined using bioelectrical impedance analysis. Concentrations of peptides in serum were assessed using the enzyme-linked immunosorbent assay. Women had higher level of leptin (P < 0.001), with no other differences for analyzed peptides. We have found a significant correlation between serum concentrations of CART and NPY (P < 0.001). Fasting leptin level was associated with age (P = 0.002), waist circumference (P < 0.001), and lean body mass (P < 0.001). Levels of ghrelin were lower in participants with dyslipidemia (P = 0.009). Levels of obestatin (P = 0.008) and leptin (P = 0.02) were higher in participants with insulin resistance. Associations between body fat and appetite-regulating peptides are more complex than simple feedback loops. Leptin is probably the first signal in the pathway that regulates body fat content, as of all analyzed peptides leptin was the only one that was associated with body composition or anthropometric measurements.

Keywords: Adiposity, appetite, body composition


How to cite this article:
Lucka A, Wysokiński A. Association between adiposity and fasting serum levels of appetite-regulating peptides: Leptin, neuropeptide Y, desacyl ghrelin, peptide YY(1-36), obestatin, cocaine and amphetamine-regulated transcript, and agouti-related protein in nonobese participants. Chin J Physiol 2019;62:217-25

How to cite this URL:
Lucka A, Wysokiński A. Association between adiposity and fasting serum levels of appetite-regulating peptides: Leptin, neuropeptide Y, desacyl ghrelin, peptide YY(1-36), obestatin, cocaine and amphetamine-regulated transcript, and agouti-related protein in nonobese participants. Chin J Physiol [serial online] 2019 [cited 2019 Nov 20];62:217-25. Available from: http://www.cjphysiology.org/text.asp?2019/62/5/217/269832


  Introduction Top


Regulation of body mass is a complex and multifactorial phenomenon with menu structures regulating energy homeostasis involved. Hypothalamic nuclei, particularly the arcuate nucleus (ARC), play a key role in the regulation of appetite. ARC key role in the regulation of energy homeostasis results from anatomical properties of this nucleus: it belongs to the medial hypothalamic nuclei and is located in the vicinity of the third ventricle (its ventral part), and even more importantly, close to the highly vascularized median eminence. Moreover, ARC is partially devoid of the blood–brain barrier, and hence that nutrients circulating in peripheral blood and regulatory factors have direct access to this nucleus. In ARC, there are two different and physiologically opposing types of neurons: orexigenic neurons that synthesize neuropeptide Y (NPY)/agouti-related protein (AgRP), and anorexigenic neurons that synthesize pro-opiomelanocortin (POMC)/cocaine- and amphetamine-regulated transcript (CART). These groups of neurons form a primary regulator of hunger and satiety.[1]

Leptin

Leptin is a hormone that regulates energy balance by inhibiting hunger in the negative feedback mechanism.[2],[3] Leptin is produced by white adipose tissue. The greater the amount of body fat, the higher the level of leptin.[4] There were no differences for men and women in basal plasma leptin concentration.[5] The mechanism of leptin action is to inhibit energy consumption and reduce food intake. This is done by the activation of anorexogenic POMC/CART neurons and the inhibition of NPY/AgRP orexigenic neurons.[6] In addition, leptin inhibits expression of orexins,[7] increases the sensitivity of glucoresponsive neuron of the hypothalamus,[8] regulates mRNA expression CART in the ARC,[9] inhibits noradrenergic neurotransmission in the periventricular nucleus,[10] reduces secretion of endogenous cannabinoids in the hypothalamus,[11] and reduces reward associated with food intake.[12]

Ghrelin

Ghrelin was discovered by Kojima et al. in 1999 and has been identified as a “hunger hormone.”[13] It consists of 28 amino acids. Ghrelin is produced mainly by P/D1 cells that line the fundus of the stomach and pancreatic epsilon cells. Three types of ghrelin have been discovered: (1) octanoylated form of ghrelin (acyl ghrelin) with orexigenic activity, (2) nonoctanoylated form (desacyl ghrelin), which is anorexigenic, and (3) obestatin.[14] The active form of ghrelin is acyl ghrelin, which can penetrate the blood–brain barrier and bind to the central receptor (growth hormone secretagogue receptor).[15] Other forms of ghrelin do not have such functions, but they have their own effects. It has been found that desacyl ghrelin reduces fat mass, increases insulin resistance (IR), through trophic and protective effects on β-cells of the pancreas. The research results show that desacyl ghrelin can inhibit ghrelin levels. This suggests that it could be helpful in future treatment of diabetes and obesity, where inhibiting the effects of ghrelin is a beneficial result.[16] Previous studies showed that ghrelin levels are positively related with testosterone levels in men and estrogen levels in women – ghrelin levels increased both in hypogonadal men treated with testosterone and postmenopausal women treated with estrogens. However, information have not yet been linked to the control of food intake.[5]

Cocaine- and amphetamine-regulated transcript

CART is an endogenous inhibitor of food intake.[17] In animals, the effect of CART results in a behavior similar to that of cocaine and amphetamine. However, when it is co-administered with cocaine, it blocks its effects. According to the studies, CART, and especially CART(55-102) have an important function in the regulation of energy homeostasis. Research on intraventricular CART administration confirmed its effect: inhibition of response to NPY and decreased appetite.[18] In addition, it has been described that the genetic variability of the CART gene may be important in the distribution of fat.[19]

Neuropeptide Y

NPY is found both in the central and peripheral nervous system. It is a 36-amino acid peptide that in the central nervous system is produced in the ARC by NPY/AgRP neurons. These neurons cause an increase in NPY signaling and a reduction in melanocortin signaling by the release of AgRP.[20] In this way, they stimulate food intake. During the fast, NPY biosynthesis increases, which is why it plays an important role in obesity.[21] There are no differences in NPY levels between men and women.[5] NPY/AgRP neurons are affected by leptin and insulin, which are secreted in proportion to the body's fat content and inhibit neuronal activity. Insulin and leptin inhibit anabolism and stimulate catabolic pathways in the hypothalamus.[22] Inverse effect has ghrelin, which secreted into the bloodstream activates NPY/AgRP neurons and causes increased food intake.[23] The studies described the following effects of NPY: increased food intake and adipogenesis leading to abdominal obesity, reduced physical activity,[24] reduced heat production in brown adipose tissue,[25] increased energy stored as fat due to increased acetyl coenzyme A carboxylase activity and acid synthesis fatty acids and triglycerides (TGA) in white adipose tissue and the liver.[20] It has also been proven that NPY can increase the level of insulin in the blood.

Agouti-related protein

AgRP is a neuropeptide, made up of 132 amino acids, that is produced in the brain by hypothalamic AgRP/NPY neurons. These neurons stimulate food intake by releasing both NPY and AgRP.[20] AgRP blocks melanocortin receptors (MC3-R and MC4-R), which are responsible for the control of metabolism and appetite.[26] AgRP increases appetite and reduces metabolism and energy expenditure, resulting in weight gain.[27] AgRP has significantly longer orexigenic properties than NPY. It was found that increasing food intake after a single central administration of AgRP is sustained for up to a week [28] - in comparison with NPY, after which it is sustained for a maximum of hours.

Peptide YY

Peptide YY (PYY), otherwise known as peptide tyrosine-tyrosine, is secreted in response to feeding by ileum and colon cells. It consists of 36 amino acids. Two major forms of PYY were found: PYY(1-36) and PYY(3-36). Individual forms have separate effects. PYY(1-36) has orexigenic activity by binding to all Y receptors. In contrast, PYY(3-36) has anorexigenic properties by high affinity for the Y2-receptor subtype and some affinity for the Y1- and Y5-receptor subtypes.[29] PYY secretion is regulated in proportion to the calories consumed by food (mainly high-fat meals). The secretion of PYY is also stimulated by gastric acid, bile salts, cholecystokinin, and insulin-like growth factor 1.[30] The glucagon-like peptide-1 is inhibiting PYY. PYY is called “ileal brake” because it inhibits ghrelin secretion, delays gastric emptying, and suppresses the secretory function of the stomach and pancreas. PYY also works centrally by inhibiting the expression of NPY and AgRP mRNA in ARC neurons by binding to presynaptic G protein-coupled Y2R auto-receptor. This is possible due to significant homology with NPY. This action, by eliminating the inhibitory effect of NPY/AgRP neurons, results in increased activity of POMC/CART neurons.[31] However, it has not been discovered whether PYY has a direct effect on POMC/CART neurons.

Obestatin

Obestatin was originally identified as an anorexigenic peptide,[32] having opposite action to ghrelin on appetite and energy balance, but its effect on food intake is still controversial since the majority of studies did not confirm its anorexigenic action.[33] Very little is known about the psychological role of obestatin in humans, also its receptor remains unknown. Its anorexigenic properties may result from decreased food intake, slowed gastric emptying, and jejunal motility,[34] although these actions were not confirmed by some authors.[35],[36] In obese people, plasma obestatin is low. Nakahara et al. found that it correlates negatively with body mass index (BMI), leptin, insulin, glucose, and homeostasis model assessment IR (HOMA-IR).[37] One very interesting action of obestatin is its role in the pathogenesis of diabetes.[38]

Study objective

Recent studies show that there are numerous and complex interactions between appetite-regulating peptides. Furthermore, the secretion of these peptides is regulated by the amount of body fat. Therefore, the objective of this study was to evaluate the associations between various adiposity parameters (body composition, anthropometric parameters, and laboratory parameters) and fasting serum levels of leptin, NPY, desacyl ghrelin, PYY(1-36), obestatin, CART and AgRP in healthy, non-obese participants. Our study hypothesis is that since leptin is directly regulated by the adipose tissue, adiposity will mostly affect leptin levels, which in turn may regulate other peptides, which in turn may be intercorrelated. Determining the relationship between peptide levels and body composition can help counteract the phenomenon of obesity, which is one of the most significant challenges in modern medicine. By studying healthy and nonobese participant, in future we can compare the results with those of obese participants and by analyzing differences we may gain better understanding on this problem and determine more appropriate methods of its treatment.


  Materials and Methods Top


Study participants

Thirty European Caucasian adult lean (non-obese) participants were included in the study (17 men and 13 women). The healthy volunteers had neither self-reported personal or familial psychiatric history nor medication history from semi-structured interview and had normal laboratory findings (blood profile, alanine aminotransferase, aspartate aminotransferase, urea, creatinine, bilirubin, and electrolytes). There were 10 smokers (33.3%) in the study group. Excluded were participants with previously diagnosed diabetes, dyslipidemia, thyroid dysfunctions, hyperprolactinemia, any acute and chronic inflammatory conditions (e.g., pneumonia, rheumatoid arthritis), immunological disorders (e.g., AIDS, allergy), and cancer. The study protocol was approved by the Medical University of Lodz Bioethics Committee (RNN/87/14/KB). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or National Research Committee and with the Helsinki Declaration, as revised in 2008. Informed consent was obtained from all individual participants included in the study.

Data collection and measurements

Medical histories, demographic information, and current smoking status were collected from the study participants. The blood samples for the chemistry panel were collected between 7 am and 8 am, after ensuring at least 8 h of overnight fasting. The samples were immediately transferred to the central laboratory, where they were analyzed. Glucose, lipids, calcium, and uric acid levels were measured using a Dirui CS-400 analyzer (Dirui, China). Homocysteine chemiluminescence assessments were performed using an Immulite 2000 analyzer (Siemens, Germany), insulin immunochemistry assessments were performed using a Cobas E411 analyzer (Roche Diagnostics, Switzerland), and albumin levels were assessed using a Cobas Integra 800 analyzer (Roche Diagnostics, Switzerland). Concentrations of peptides in serum were assessed using the enzyme-linked immunosorbent assay, with commercial kits manufactured by RayBiotech (USA) and according to the manufacturer's instruction (intra assay coefficient of variation [CV] <10%, inter-assay CV <15% for all kits). Before assays, serum samples were stored at −80°C for up to 6 months.

Body composition (body fat and lean body mass) was determined using bioelectric impedance analysis (BIA), which provides accurate measurements of body fat, lean body mass, and body water.[39] Body composition was measured using Maltron BF-906 Body Fat Analyzer (Maltron, UK), single-frequency bioelectrical impedance analyzer at 50 Hz. Briefly, BIA determines the electrical impedance, or opposition to the flow of an electric current through body tissues, which can then be used to calculate an estimate of total body water, which can be used to estimate fat-free body mass and, by difference with body weight, body fat. Standard operating conditions were observed by a trained operator including preparation of the participant, electrode placement, and operation. The measurement using BIA was taken immediately before anthropometry measurements with participants lying supine, in a rested state. Body fat and lean body mass were expressed as total mass (in kg) and as a percentage of body mass.

Height was measured with a wall-mounted height measure to the nearest 0.5 cm. Weight was measured with a spring balance that was kept on a firm horizontal surface. Participants wore light clothing, stood upright without shoes, and weight was recorded to the nearest 0.5 kg. BMI was calculated as body weight in kilogram divided by the height in meter squared (kg/m 2). Waist and hip circumferences were measured using a nonstretchable fiber measuring tape. Waist-to-hip ratio (WHR) was calculated as waist circumference divided by hip circumference. Fat mass index (FMI) is a better indicator of obesity since it takes body fat into account. FMI was calculated as total body fat in kilogram (measured using BIA method) divided by the height in meter squared (kg/m 2). Obesity was defined using FMI (>6 for men and >9 for women).[40] This index is based on the amount of adipose tissue and compared with WHR or BMI reflects adiposity better.

Impaired fasting glucose was defined as fasting plasma glucose ≥100 mg/dL. Raised TGA level ≥150 mg/dL and/or total cholesterol (TC) ≥200 mg/dL, reduced high-density lipoprotein (HDL) cholesterol level <40 mg/dL for men and <50 mg/dL for women, and/or raised low-density lipoprotein (LDL) cholesterol level ≥135 mg/dL were interpreted as dyslipidemia. Corrected calcium was calculated using the formula: corrected calcium (mg/dL) = measured calcium (mg/dL) + 0.8 (4.0 − serum albumin [g/dL]). IR was estimated from fasting glucose and insulin levels by HOMA-IR, using the formula: HOMA-IR = (fasting plasma glucose [mg/dL] × insulin [mU/L])/405. IR was defined as HOMA-IR > 2.0.

Data analysis

Statistical procedures were performed with STATA 14.2 (StataCorp, College Station, USA) and GraphPad Prism 7.00 (GraphPad Software, La Jolla, California, USA). Simple descriptive statistics (means, standard deviations, 95% confidence intervals) were generated for continuous variables. For discrete variables, number of patients and percentages are given. Normality of distribution was tested with Shapiro–Wilk test. Variables with normal distribution were analyzed using two-tailed t-test; otherwise, Wilcoxon rank-sum test was used. The difference between proportions was analyzed using Fisher's exact test. Associations were tested by Pearson's (for variables with normal distribution) or Spearman's (for other variables) correlation coefficients and adjusted linear regression models. The statistically significant level was set at P < 0.05 (two-sided).


  Results Top


Detailed demographic and anthropometric data are shown in [Table 1]. As expected, men in the study group had higher body mass, BMI, waist circumference, and (probably as a result of these differences) higher blood pressure. There was no difference in the percentage of smokers (men: 41.2%, women: 23.1%; P = 0.44). FMI-based obesity was found in 15 (50.0%) participants (11 (64.7%) men and 4 (30.8%) women, with no intersex differences). Results of laboratory tests are shown in [Table 2]. In general, laboratory parameters were comparable between men and women. Only exceptions were uric acid (higher in men) and HDL cholesterol (higher in women). Obese participants had higher systolic blood pressure (P = 0.009), lower HDL cholesterol (P = 0.006), and higher amounts of body fat and lean body mass (P < 0.001 and P = 0.03, respectively). [Table 3] shows the results of body composition. Men had higher amount (absolute and relative) of lean body mass, whereas women had higher relative (expressed as the percentage of total body mass) amount of body fat.
Table 1: Summary of clinical data and anthropometric measurements

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Table 2: Results of laboratory tests

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Table 3: Body composition analysis

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Fasting serum levels of leptin, NPY, desacyl ghrelin, PYY(1-36), obestatin, CART, and AgRP are shown in [Table 4]. Compared with men, women had a higher level of leptin (P < 0.001) [Figure 1]. There were no differences for other analyzed appetite-regulating peptides. We have analyzed inter-peptides correlations. Of all peptides, we have found there is a significant correlation between serum concentrations of CART and NPY peptides (r = 0.74, P < 0.001). The correlation between CART and NPY peptides was still significant when obese and non-obese participants were analyzed separately (r = 0.71, P = 0.01 and r = 0.83, P = 0.001, respectively). Moreover, in obese participants, there was another correlation between PYY(1-36) and CART (r = 0.67, P = 0.02), which was not present in nonobese participants. Linear regression analysis (adjusted for age and sex) confirmed the association between CART and NPY [R2 = 0.56, standardized coefficient β =1025.34, P = 0.011, [Figure 2].
Table 4: Fasting serum concentrations of appetite-regulating peptides

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Figure 1: Fasting serum levels of leptin in the study groups. Vertical bars indicate standard errors

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Figure 2: Regression analysis of the association between fasting cocaine- and amphetamine-regulated transcript and neuropeptide Y serum levels (adjusted for age and sex)

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We have analyzed correlations between levels of appetite-regulating peptides and anthropometric parameters separately in men and women. We have found that such correlations were significant only for leptin (total body fat [kg]: men r = 0.71 P < 0.01, women r = 0.68 P < 0.01; total body fat [%]: men r = 0.72 P < 0.01, women r = 0.64 P < 0.05; lean body mass [%]: men r = −0.72 P < 0.01, women r = −0.64 P < 0.01; weight: women r = 0.71 P < 0.01; BMI: women r = 0.70 P < 0.01; FMI: men r = 0.66 P = 0.004; waist circumference: women r = 0.77 P < 0.01; hip circumference: women r = 0.69 P < 0.01). To analyze these associations, we used stepwise linear regression analysis models. For each peptide, a regression model included age, BMI, FMI, waist circumference, WHR, blood pressure, total body fat, and lean body mass. Of all models, regression model was significant only for leptin (R2 = 0.63, P < 0.001) with associations between leptin and age (β = −0.08, P = 0.002), waist circumference (β = 0.15, P < 0.001), and lean body mass (β = −0.22, P < 0.001). However, when sex was added to this model, it was found that while the model remains significant (R2 = 0.71, P < 0.001), only male sex (β = −4.06, P < 0.001) and waist circumference (β = 0.10, P < 0.001) were significantly associated with leptin level.

There were no differences for leptin, NPY, desacyl ghrelin, PYY(1-36), obestatin, CART, and AgRP concentrations in participants with FMI-defined obesity. Using a nonparametric Kruskal–Wallis one-way analysis of variance, we have found no differences in levels of leptin, NPY, desacyl ghrelin, PYY(1-36), obestatin, CART, and AgRP in participants with normal or above-normal BMI values (>25 kg/m 2). Levels leptin, NPY, desacyl ghrelin, PYY(1-36), obestatin, CART, and AgRP between participants were not different in participants with or without impaired fasting glucose. For dyslipidemia, only levels of ghrelin were different in the presence of this abnormality, with lower concentration in participants with dyslipidemia (218.24 ± 128.007 vs. 383.78 ± 104.46 pg/mL, P = 0.009). However, there were no significant correlations between ghrelin concentration and levels of TGA, TC, LDL or HDL. Levels of obestatin (167.53 ± 20.66 vs. 129.33 ± 35.32, P = 0.008) and leptin (3.38 ± 2.10 vs. 1.77 ± 1.70, P = 0.02) were significantly higher in participants with IR. We have analyzed associations between laboratory parameters (blood lipids, fasting glucose, insulin, uric acid, homocysteine, folic acid, and corrected calcium) and levels of appetite-regulating peptides using linear regression analysis models. For each peptide, a regression model included sex, age, BMI, and waist circumference. Each model was analyzed using stepwise regression analysis, and we found no significant associations.


  Discussion Top


This is the first study that combines measurements of seven appetite-regulating peptides (leptin, NPY, desacyl ghrelin, PYY(1-36), obestatin, CART and AgRP) and various adiposity parameters (body composition (body fat, lean body mass), anthropometric parameters (BMI, FMI, WHR) and laboratory parameters related to obesity, i.e., TGA, cholesterol, glucose, insulin, homocysteine, uric acid, and calcium).

In our study, women had a higher level of leptin compared with men. In a previous study woman had significantly higher concentrations of leptin, especially in those who were obese.[41] However, in another study, there were no differences for men and women in basal plasma leptin concentration.[5] There were no sex differences for other analyzed appetite-regulating peptides. The previous study showed that women had significantly higher concentrations of desacylated ghrelin, but there was no sex difference in acylated and total ghrelin concentrations.[41] Another study showed that ghrelin levels are positively related to testosterone levels in men and estrogen levels in women – ghrelin levels increased both in hypogonadal men treated with testosterone and postmenopausal women treated with estrogens. However, information have not yet been linked to the control of eating.[5] This shows that the association is not clear and requires furthers studies.

Of all peptides, we have found there is a significant correlation between serum concentrations of CART and NPY peptides. Moreover, in obese participants, there was another correlation between PYY and CART, which was not present in non-obese participants. To analyze the correlations between the levels of appetite-regulating peptides and anthropometric parameters separately in men and women, we have found that such correlations were significant only for leptin. There were no differences for analyzed peptides concentrations in participants with obesity, impaired fasting glucose, levels of TGA, TC, LDL, or HDL. Levels of ghrelin were lower concentration in participants with dyslipidemia. Levels of obestatin and leptin were significantly higher in participants with IR. We found no significant associations between analyzed laboratory parameters and levels of appetite-regulating peptides.

Knowing that appetite-regulating peptides form a complex system with various internal inhibitory or activating interactions, we expected there will be associations between their peripheral concentrations. However, we found only one (although relatively strong) positive association between fasting levels of CART and NPY. Previous studies showed that in rodents CART is expressed in the anorexigenic α-MSH-synthesizing neurons and probably inhibits food intake.[42] However, a recent human study showed that CART is expressed in approximately one-third of orexigenic NPY/AgRP neurons.[43] Therefore, contrary to the rodent, in human, CART may play an orexigenic role. Our finding may support this hypothesis and reflect co-expression of both peptides. A recent study also links a mutation in the CART gene to obesity in humans.[44] Both these peptides (NPY and CART) might become targets for drug development in the area of obesity.[45],[46] Question whether peripheral levels of these peptides reflect their expression and secretion at the brain level remains open (e.g., CART is also expressed in pituitary endocrine cells, adrenomedullary cells, islet somatostatin cells, and in rat antral gastrin cells [47]). The lack of other associations may also be explained by the same discrepancy for other peptides.

The level of circulating leptin is directly proportional to the size of body fat,[4] while secretion of other peptides is regulated by other mechanisms. In this study, we have found that fasting level of leptin was negatively associated with lean body mass, but found no association with the amount (absolute or relative to body weight) of body fat. Leptin levels were different between men and women (with a higher concentration in women). In general, women (also in our study) have more body fat, and secretion of leptin is directly regulated by the amount of body fat. When the relationship between anthropometric parameters, body composition, and leptin level was adjusted by sex, we found that only waist circumference was associated with fasting leptin concentration. Since waist circumference is correlated with visceral fat volume, this might indicate that leptin secretion is regulated by the visceral adipose tissue. However, Minocci et al. have shown that subcutaneous fat is a determinant of leptin concentration, whereas the contribution of visceral fat is not significant.[48] Our finding that leptin levels were increased in participants with abdominal obesity might indicate that abdominal subcutaneous fat tissue is the major regulator of leptin secretion. However, methodological limitations make it impossible to measure segmental subcutaneous fat tissue and visceral fat.

We did not find any correlation between the levels of other peptides and body composition. Lack of associations between body fat content and ghrelin level was also confirmed by Stylianou et al.,[49] although they do not specify which form of ghrelin (octanoylated or nonoctanoylated) was measured. In a large (n = 1434 subjects) study, Martin et al. found that five different single-nucleotide polymorphisms within ghrelin gene are not associated with the amount of body fat.[50] On the other hand, Labayen et al. found that fasting serum ghrelin levels are correlated with changes in total body fat mass and FMI after energy-restricted diet intervention in obese women,[51] and Cederberg et al. found that non-octanoylated ghrelin is associated with changes in body composition and body fat distribution during long-term exercise intervention.[52] In animal models, Ruegsegger et al. found a significant negative correlation between body fat and NPY.[53] The lack of such association in our study may result from methodological differences (we have measured peripheral NPY levels, while Ruegsegger et al.[53] evaluated levels of hypothalamic NPY mRNA). For PYY, there is one study showing that the concentration of fasting serum PYY was positively associated with body fat measures (waist circumference, percent body fat, and trunk fat), but only in women.[54]

Obestatin regulates β-cell survival,[55] and insulin secretion,[38] inhibits glucose-dependent insulin secretion,[56] and this inhibition probably results from a direct effect of obestatin on the pancreatic islets. Obestatin exerts a dual effect on glucose-induced insulin secretion: at low glucose concentration obestatin potentiates the insulin response to glucose, while at high concentrations, it inhibits insulin release evoked by glucose (because at high glucose concentrations beta cells are less responsive to obestatin).[57] Our results confirm that peripheral level of obestatin may be related to glucose metabolism and its abnormalities (mainly to IR) as obestatin level was higher in participants with IR, but there was no difference for impaired fasting glucose or correlations between obestatin and fasting plasma glucose, insulin, or HOMA-IR.

As every study, this work has certain limitation. Low number of the study participants limited the probability of finding inter-group differences due to lack of statistical power. We have no behavioral data (e.g., physical activity, food intake logs). Furthermore, dual-energy X-ray absorptiometry could be used to measure body composition and percentage of fat more accurately. The major strength of the study is the ability to analyze seven different appetite-regulating peptides in the context of detailed anthropometric, body composition, and biochemical measurements. One of the major conclusions from the above studies is that associations between body fat and appetite-regulating peptides are far more complex than simple feedback loops. At the peripheral level, there may be no direct associations between expression and secretion of centrally acting appetite-regulating peptides. Our results may also confirm that leptin (and other adipokines) is the first signal in the pathway that regulates body fat content, as of all analyzed peptides leptin was the only one that was directly associated with body composition or anthropometric measurements. The results of this type of research can give measurable benefits for patients with obesity. Information on changes in individual peptides will be helpful in determining ways to prevent and treat obesity, among others, by determining the mechanism of action of new drugs or introducing specific dietary recommendations. Finding relationships between specific body components, concomitant diseases and the level of individual appetite-regulating peptides will help determine treatment methods for individual patients. Such targeted therapy can reduce drug side effects and treatment complications.

Financial support and sponsorship

This study was financially supported by the Medical University of Lodz (grant number 502-03/8-040-01/502-64-139-18).

Conflicts of interest

There are no conflicts of interest.



 
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