The Clinical Utility of salivary oxytocin as a putatively surrogate early Risk Identification biomarker of nascent Metabolic Syndrome with and without prediabetes

The Clinical Utility of salivary oxytocin as a putatively surrogate early Risk Identification biomarker of nascent Metabolic Syndrome with and without prediabetes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Clinical Utility of salivary oxytocin as a putatively surrogate early Risk Identification biomarker of nascent Metabolic Syndrome with and without prediabetes Nailya R. Bulatova, Violet N. Kasabri, Abla M. Albsoul, Lana Halaseh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2587738/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims and methods This study aimed to compare and correlate pharmacotherapy biomarkers’ plasma and salivary levels (appraised using colorimetric assays of Lipocalin, Nesfatin, Omentin, Oxytocin, RBP-4 (retinol-binding protein-4), Resistin, SIRT 1 (sirtuin 1), Visfatin and ZBED3 (zinc finger, BED-type (ZBED) protein 3), adiposity, and atherogenicity indices in 61 normoglycemic and newly diagnosed drug naive pre-diabetic (PreDM) MetS (metabolic syndrome) patients vs. 29 lean, and normoglycemic controls. Intergroup Comparisons was conducted by ANOVA. Spearman rank correlation was also examined. Results About three quarters of the participants were females, with gender distribution similar between the two study groups (P = 0.585). Among MetS patients, almost half were normoglycemic, about 43% were prediabetic and about 8% were diabetic. The average age of study participants was 48.6 years, with MetS group being significantly older than the control group (P < 0.001). In accordance to the study selection criteria, glycemic (FPG and A1c) and lipid parameters (TG, HDL-C and non-HDL-C), adiposity indices (BMI, WHR, WtHR, C-index, BAI, LAP, VAI) and atherogenicity indices (AIP, TC/HDL-C, LDL-C/HDL-C, non-HDL-C/HDL-C and TG/HDL-C) were all significantly higher in the MetS group compared to the control group (P < 0.05). Among the plasma cardiometabolic risk biomarkers of pharmacotherapy, plasma (but not salivary) lipocalin levels and Salivary nesfatin (unlike plasma nesfatin) were significantly higher P < 0.05) in the MetS group compared to the normoglycemic lean controls. Notably, plasma SIRT1 levels were pronouncedly greater (P < 0.05) in MetS recruits in comparison to control’s levels. Conversely; salivary SIRT1 concentrations in MetS pool markedly exceeded those of controls’ salivary levels. Oddly and collectively salivary and blood levels of omentin, oxytocin, RBP-4, resistin, visfatin and ZBED3 lacked comparably pronounced discrepancies in MetS cases vs. those of study controls. Exceptionally oxytocin, amongst 9 cardiometabolic risk biomarkers of pharmacotherapy studied, had proportional significant correlations between plasma and saliva levels, in both total sample and MetS patients (P < 0.05). Plasma OXT in the total sample correlated significantly though inversely with both SBP and FBG (unlike salivary OXT). Interestingly of MetS pool; markedly Proportional correlations of plasma (but not salivary) OXT with TG, and adiposity indices of LAP and VAI, and all atherogenecity indices were delineated. Collectively both blood and saliva OXT in the total study pool, as well as the remaining biomarkers; lacked comparably substantial associations with both adiposity and atherogenecity indices and clinical parameters of fasting lipid profile. Biological sciences/Biochemistry Biological sciences/Molecular biology Biological sciences/Physiology Health sciences/Biomarkers Health sciences/Endocrinology Health sciences/Medical research Health sciences/Molecular medicine Lipocalin Nesfatin Omentin Oxytocin RBP-4 (retinol-binding protein-4) Resistin SIRT 1 (sirtuin 1) Visfatin and ZBED3 (zinc finger BED-type (ZBED) protein 3 adiposity and atherogenicity indices cardiometabolic risk metabolic syndrome prediabetes Figures Figure 1 Introduction The metabolic syndrome (MetS) is a constellation of cardiometabolic risk factors, adiposity and insulin resistance being its main features. The presence of MetS increases risk of coronary heart disease and type 2 diabetes mellitus (T2DM) 1 . Although the pathogenic mechanisms of MetS have not been elucidated, both increased inflammation and insulin resistance play a pivotal role. It seems that the monocyte/macrophages and adipose tissue (AT) interact to accentuate both the pro-inflammatory state and increased insulin resistance. There is a marked increase in macrophages and crown-like structures in the subcutaneous adipose tissue (SAT) of patients with MetS 2 . Adipose tissue production of cytokines (adipokines or adipocytokines) plays central role in MetS and T2DM 1 . Adipokines play a critical role in storage, food intake, energy expenditure, and lipid and glucose metabolism 3 . In MetS there is an increase in plasma leptin, plasminogen activator inhibitor-1, retinol-binding protein-4(RBP-4), chemerin, serum amyloid-A, C-reactive protein (CRP), interleukin-1, -6, -8, lipopolysaccharide, fetuin A (FetA) and a decrease in adiponectin and omentin- 2 . The growing evidence shows that the processes resulting in T2DM are started very early with a long lag phase between the disease onset and the clinical diagnosis. Multiple researches have evaluated various serum biomarkers as predictive for T2DM 4–5 . Nearly 40% of the proteins that have been suggested to be candidate markers for diseases such as cancer, cardiovascular disease, and stroke can be found in whole saliva. With numerous plasma biomarkers verified for metabolic risk, relatively few studies have been carried in the area of salivary biomarkers. It has been observed that approximately 40% of cancer, stroke and cardiovascular disease biomarkers are present in whole saliva 6 .Finally it should be noted that 27% of the salivary proteome overlapped with the plasma proteome 6 . Human saliva is a rich reservoir of biomarkers including over 3652 proteins and 12,562 peptides and shares nearly 51% of proteins with the serum proteome and 79% of peptides with the serum peptidome 5 . Periodic evaluation of select plasma protein biomarkers during time course of type 2 diabetes mellitus (T2DM) development may increase the predictive ability of diabetes risk scores 7 . The use of salivary biomarkers has increasing value as a result of discovery of significant similarities between the salivary and serum proteomes 7 . The collection of saliva is a noninvasive, easily repeatable and less stressful technique than blood withdrawal. As an example, the levels of salivary resistin, visfatin, and adiponectin correlated with serum hormonal levels 8 . As an example, a recent meta-analysis of biomarkers in periodontitis and/or obesity demonstrated that, obesity and periodontitis, together or separately, are associated with altered serum and gingival crevicular fluid levels of leptin, adiponectin, and resistin, but concluded that the role of vaspin, omentin-1 and some other molecules, which can be key points underlying the association between obesity and periodontitis, remains to be further investigated 9 . In this research we focused on adipose- and/or skeletal muscle-derived signaling as examples of metabotrophic factors (MTFs) involved in the pathogenesis of obesity and related cardiometabolic diseases 9 . Hence collectively a battery of proportionally associated with insulin resistance related adiposity and cardiometabolic risk factors. Hence it was the aim of this study to examine the early cardiometabolic risk associations of these peptides; namely Lipocalin, Nesfatin, Omentin, Oxytocin, RBP-4 (retinol-binding protein-4), Resistin, SIRT 1 (sirtuin 1), Visfatin and ZBED3 (zinc finger, BED-type (ZBED) protein 3) with adiposity – and atherogenecity –related insulin resistance in normoglycemic and dysglycemic metabolic syndrome population. Scarcity of studies that investigated correlations between plasma and salivary cardiomatabolic biomarkers’ levels in MetS patients is clearly noticeable. Moreover given that saliva biomarkers seem to be promising in the area of metabolic syndrome (MetS) detection and diagnosis due to less invasive nature, less expensive and faster sample collection in comparison to plasma biomarkers 10 . Taken together it was this study aim to investigate the potential correlations between plasma and saliva levels of these cardiometabolic risk biomarkers for pharmacotherapy institution and follow up reasons of MetS patients with a defined cluster of adiposity and atherogenecity indices. Lipocalin 2 is a 25 kDa glycoprotein expressed in several cells as in adipocytes transporting small lipophilic ligands, as in lipopolysaccharides, through hydrophilic body fluid 11–12 . It can be a pro-inflammatory factor elevated in obese ⁄ inflammatory states 11 secreted by White adipose tissue (WAT). 13 In addition lipocalin 2 is involved in apoptosis, ion transport, inflammation, cell survival, tumorigenesis, reproduction and atherosclerosis 12 . Nesfatin is an anorexic neuropeptide of significant regulation of energy metabolism and food intake and widely distributed in the central nervous system and peripheral tissues. It is linked to regulation of food intake and lipid metabolism, inhibiting fat accumulation, accelerating lipid decomposition, and in inhibiting the development of lipid-related disorders of obesity and metabolic syndrome (MetS) 14 . Furthermore it influences cardiac functions –related glycemia and generates weight loss 15 . Lower levels of nesfatin (pg/mL) were reported in both pre-diabetic and non-diabetic MetS patients 16–17 . Omentin is 313 amino acids-adipocytokine expressed by a diversity of tissues (as in mesothelial cells, vascular cells, airway goblet cells, omental and epicardial fat, small intestine, colon, ovary, and plasma). It sustains body metabolism and insulin sensitivity, with antiinflammatory, anti-atherosclerotic, and cardiovascular protective effects via AMP-activated protein kinase/Akt/nuclear factor-κB/mitogen-activated protein kinase (ERK, JNK, and p38) signaling. 18 In a close-relation with T2DM; 12 this visceral adipokine expression in pre)adipocytes is decreased by glucose/insulin and stimulated by fibroblast growth factor-21 and dexamethasone. It can also enhance insulin-mediated glucose uptake in human subcutaneous and omental adipocytes 19 . It was reported to increase insulin sensitivity by activating Akt and enhancing insulin-(but not basal) stimulated glucose uptake both by subcutaneous and omental adipocytes; due to lack of intrinsic insulin-mimetic activity 12 . Significantly salivary and serum Omentin-1 were found related in chronic periodontitis and T2DM 20 . Oxytocin (OXT) is involved in the maintenance of labor and lactation in female reproduction. It has substantial roles in regulating social memory and anxiety 21 . It is principally a nanopeptide released by paraventricular nucleus (PVN), and the supraoptic nucleus (SON) in the hypothalamus. With pronounced regulatory roles in energy homeostasis as an anorexigenic factor; low OXT circulating concentrations were found in diet-induced or genetically modified animal models of obesity and in humans. 22–23 In amenorrheic athletes; OXT secretion proportionally correlated with measures of energy availability in linkage to weight and body mass index and energy expenditure. 22 OXT receptors are found in multiple organs as in uterus, breast, aorta, and esophagus 24 . OXT receptors over-expression in close linkage to adiposity and lipolysis in adipocytes was inherently noticeable. 24–25 In MetS and prediabetes; it was pronouncedly reduced. 26–28 Meanwhile salivary OXT positively associated with plasma, but not with urine OXT in women acutely ill with anorexia nervosa. 29 Retinol-binding protein 4 (RBP-4) is one of the most important adipokines that affects systemic insulin sensitivity and glucose homeostasis with link to insulin resistance and MetS in obesity 30 . It is a transport protein for vitamin A (retinol), synthesized mainly by hepatocytes followed by adipocytes 12 . RBP-4 may play a role in the pathogenesis of T2D by participates in the development of insulin resistance by impairing insulin signaling at both the receptor and post-receptor levels, as well as by stimulation of liver gluconeogenesis 31 . RBP-4 is potentially associated with an increased risk of developing cardiovascular disease, particularly among patients with obesity, mainly due to an increased expression of pro-inflammatory cell surface adhesion molecules and soluble pro-inflammatory factors and possibly due to an unfavorable lipid profile and an increased intima-media thickness 32 . Resistin is secreted as a 94-amino acid polypeptide with an inhibitory effect on adipocyte differentiation and an association with insulin resistance 12 . Human resistin is mainly secreted by peripheral blood mononuclear cells; it competes with lipopolysaccharide for the binding to Toll-like receptor 4 and is involved in the inflammation 12 . Activation of resistin in the pancreatic islet cells inhibits insulin signaling via suppression of cell surface glucose transporters and a pro-inflammatory mechanism that results in β-cell loss, thus, a pro-diabetic effect 5 . Oddly the gradual increase in resistin levels (ng/mL), though not ascribed any statistically marked variation, was appreciable in both normoglycemic and preDM MetS groups vs. controls 33 . SIRT 1 (sirtuin 1) is a protein from the sirtuin family of nicotinamide adenine dinucleotide-dependent deacylases (SIRT1-7) that are thought to be responsible mainly for the cardiometabolic benefits of lean diets and exercise, delaying key aspects of aging, e.g., decline in vascular endothelial function, metabolic syndrome, ischemia-reperfusion injury, obesity, and cardiomyopathy. Sirtuin activity steadily decreases with increasing age, and the decline is further exacerbated by obesity and sedentary lifestyles 34 . It was recently found that the defect in endothelial sirtuin 1 deacetylase activity is associated with (a) elevated basal and stimulated levels of superoxide generation (via the FoxO1 over-acetylation mechanism) and (b) increased nuclear translocation of NF-kB (via p65 over-acetylation mechanism). Based on these findings, the novel function of sirtuin 1 was proposed, namely, the maintenance of endothelial glycocalyx, particularly manifest in conditions associated with sirtuin 1 depletion 35 . It was found of reduced levels in MetS patients 36 . Visfatin is a highly conserved, 52 kDa protein expressed in a variety of tissues and cell types, including adipocytes, being much more abundant in visceral fat than in subcutaneous fat 12 . Visfatin has the dual effects as an adipocytokine, namely, as a global insulin-imitator and local adipogenic 37 . It has nicotinamide phosphoribosyltransferase (NAMPT) activity and, hence, an insulin-mimicking/insulin-sensitizing effects 5 . It binds to insulin receptor at a different site from that of insulin and stimulates glucose uptake in adipocytes and muscle cells and suppresses glucose release from hepatocytes 12 . The ZBED3 (zinc finger, BED-type (ZBED) protein 3) gene family comprises a closely related group of genes that contribute to the regulation of various functions by encoding regulatory proteins 38 . It was suggested that both Zbed3 and Axin2 have important roles in regulating Wnt activity while Wnt/b-catenin signaling has been shown to regulate adipogenesis and, thus, has a relationship with obesity and insulin resistance 39 . Study design The study was approved by the Jordan University Hospital (JUH) Institutional Review Board (IRB). All procedures performed in the study were in accordance with the ethical standards of the IRB and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The potential study participants were approached randomly during their visits to the Family Medicine Clinic at the Jordan University Hospital (JUH). The participants were interviewed and their medical files were reviewed in order to assess the inclusion and exclusion criteria and in order to distribute them into the study groups. All potential candidates were informed thoroughly about the study; participants who agreed to take part in the study were asked to sign an informed consent form in Arabic. Data collection of patients’ medical histories was conducted until May 2020. All study participants were coded according to the study arm. Study population This is a cross sectional study aimed to examine the relation between plasma levels of eighteen metabolic risk biomarkers (arranged alphabetically): Lipocalin, Nesfatin, Omentin,, Oxytocin, RBP-4 (retinol-binding protein-4), Resistin, SIRT 1 (sirtuin 1), Visfatin and ZBED3 (zinc finger, BED-type (ZBED) protein 3) in two groups of adult (18–75 years) Jordanian patients, namely, 1) Metabolic syndrome (MetS) group that included 61 individuals who were overweight or obese (BMI > 25 kg/m 2 ) with 3 or more of MetS criteria 40 . According to the new IDF definition, for a person to be defined as having the MetS, they must have central obesity (defined as waist circumference with ethnicity specific values)* plus any two of four additional factors. These four factors are shown in Table 1 . Within the MetS group, any of the following individuals were included ( Fig. 1 ) : Control group that included 31 healthy individuals who were normoglycemic (a fasting plasma glucose (FPG) < 100 mg/dL or a hemoglobin A1c (A1C) < 5.7% 33)) and lean (BMI < 25 kg/m 2 ) Prediabetes - a FPG of 100–125 mg/dL, or a 2-hour plasma glucose level of 140 mg/dL–199 mg/dL during a 75-g oral glucose tolerance test (OGTT), or A1C of 5.7–6.4%; Type 2 diabetes mellitus - a FPG level of ≥ 126 mg/dL, or OGTT, or random plasma glucose of 200 mg/dL or higher in a patient with classic symptoms of hyperglycemia or hyperglycemic crisis, or HbA1c level of 6.5% or higher 41 . The following were exclusion criteria : Non-fasting individuals Pregnant or breast feeding/lactating women. Any prior use of anti-diabetic agent such as (sulfonylureas, meglitinides, biguanides, thiazolidinediones, alpha-glucosidase inhibitors, or insulin) either for diabetes itself or for any other condition. Any prior use of lipid lowering agents. Clinical evidence of autoimmune, life-threatening diseases, alcohol, drug abuse, and recently diagnosed and untreated endocrine disorder other than prediabetes or diabetes mellitus. Individuals with known inflammatory diseases such as the inflammatory bowel disease. Anthropometric measurements Weight and height were measured using a balance mounted stadiometer. Waist circumference (WC) was measured using a nonstretchable tape at the midpoint between the last rib and the upper iliac crest, and hip circumference (HC) was measured around the widest section of the buttocks. Body mass index (BMI) was calculated as body weight (kg) divided by the square of height (m 2 ). WHR and WHtR were calculated by dividing the WC (cm) by HC (cm) and height, respectively. Systolic blood pressure (SBP) and diastolic blood (DBP) pressures were measured using an electronic sphygmomanometer. Adiposity and atherogenecity indices were calculated using formulae 42–44 . Analysis of HbA1c, FPG and lipid profile were conducted, Study design This cross sectional study meant to compare plasma and saliva levels of cardiometabolic risk factors in Control group of 30 participants who were apparently healthy, lean (BMI < 25 Kg/m2), and normoglycemic (HbA1c < 5.7%, FBS 25 kg/m 2 ) or obese (BMI > 30 kg/m 2 ) drug-naïve MetS subjects, as defined by Alberti et al. 45 The cases group included 29 normoglycemic MetS and 29 pre-diabetes MetS (Fig. 1 ). Individuals with any of the following assessed candidates with these criteria were excluded from the study: 16, 33, 36,45 Non fasting individuals Any woman who is pregnant or breast feeding. Clinical evidence of autoimmune or life threatening disease (alcohol/drug abuse/recently diagnosed and untreated an endocrine disorder. Individuals with known inflammatory diseases such as the bowel inflammatory disease. Obesity secondary to endocrine derangement other than DM. Any prior treatment with any kind of antidiabetic medications used for diabetes or any other medical condition. Clinical settings and duration The study was conducted at the Family Medicine Clinic and the General Laboratories of the Jordan University Hospital (JUH) in accordance with the Declaration of Helsinki. The project was approved by the IRB (Institutional Review Board (IRB) Bioethics Committee of the Jordan University Hospital (JUH) and the Scientific Research Committee at the School of Pharmacy, the University of Jordan. The eligible participants were informed in detail about the study and gave their written consent in Arabic. Participation in the study was voluntary. Furthermore, they were interviewed about their medical and family history alongside with reviewing their medical file to collect clinical information and laboratory data. Patient recruitment started at the beginning of July 2017 and ended by the beginning of December 2017.The anthropometric data such as height, waist circumference, blood pressure, and BMI were measured using specific tools. A venous blood was drawn from each candidate after 12 hours fasting to assess the levels of fasting plasma glucose (FPG) and lipid profile. The biochemical analysis of fasting lipid profile (HDL-C, LDL-C, TG, and TC), FPG, and HbA1c were performed for each participant. Table 2 displays the indices that were used in this study. Lipocalin 2, oxytocin, and nesfatin were procured from Abcam (Cambridge, MA, USA). Omentin, RBP-4 (retinol-binding protein-4), Resistin, SIRT 1 (sirtuin 1), Visfatin and ZBED3 (zinc finger, BED-type (ZBED) protein 3) were obtained from MyBiosourse, Inc. (San Diego, CA, USA). Markers’ plasma and salivary levels were assayed according to manufacturers’ instructions with intra- and interassay precisions of < 10-<12%. Harvested plasma (from lithium heparin collection tubes centrifuged at 4000 rpm for 10 minutes) were immediately stocked at -80oC until analysis. All saliva samples were collected via passive drool method into SalivaBio Saliva Collection Device (Salimetrics, Carlsbad, CA, USA). Immediately after collection, saliva samples were centrifuged for 15 min at 4000 rpm to remove any particles or sediments and supernatants using 2ml cryovials were stored at − 70 ◦C until analysis. Statistical analysis Data were entered and analyzed via IBM SPSS© statistics 22 (SPSS, Inc., USA). Shapiro-Wilk test for was used for the assessment of normality of data distribution. Categorical data were expressed as numbers (%), normally distributed continuous data were expressed as mean (SD), and not normally distributed continuous data were expressed as median [interquartile range]. Gender differences between the study groups were tested using Chi-square test. While comparing continuous independent variables between the study groups we used the independent sample t-test for normally distributed data and Mann-Whitney test for data that were not normally distributed. Spearman correlation test was used for the assessment of correlations between plasma and salivary metabolic risk biomarkers as well as of selected biomarkers and clinical and laboratory parameters in both the total study sample and the MetS patients alone. Correlations were considered very strong, if correlation coefficient was at least 0.8; moderately strong, if the coefficient was 0.6 up to 0.8; fair, if the coefficient was 0.3 to 0.5 and poor if the coefficient was less than 0.3 46 . For all statistical tests, p < 0.05 was determined as statistically significant. Results Demographic and clinical characteristics ( Table 2 ). About three quarters of the participants were females, with gender distribution similar between the two study groups (P value = 0.585). Among MetS patients, almost half were normoglycemic, about 43% were prediabetic and about 8% were diabetic. The average age of study participants was 48.6 years, with MetS group being significantly older than the control group (P value < 0.001). In accordance to the study selection criteria, glycemic (FPG and A1c) and lipid parameters (TG, HDL-C and non-HDL-C), adiposity indices (BMI, WHR, WtHR, C-index, BAI, LAP, VAI) and atherogenicity indices (AIP, TC/HDL-C, LDL-C/HDL-C, non-HDL-C/HDL-C and TG/HDL-C) were all significantly higher in the MetS group compared to the control group (P value < 0.05) Plasma and salivary levels of cardiometabolic risk biomarkers of pharmacotherapy in MetS patients ( Table 3 ). Among the plasma cardiometabolic risk biomarkers of pharmacotherapy, plasma (but not salivary) lipocalin levels were significantly higher (> 17-folds; P value < 0.05) in the MetS group compared to the control group. Salivary nesfatin (unlike plasma nesfatin) levels were substantially higher in MetS participants vs. normoglycemic lean controls. Notably, plasma SIRT1 levels were pronouncedly greater (P value < 0.05) in MetS recruits in comparison to control’s levels. Conversely and oddly; salivary SIRT1 concentrations in MetS pool markedly exceeded those of controls’ salivary levels. Collectively salivary and blood levels of omentin, RBP, resistin, visfatin and ZBED3 lacked comparably significant variations in MetS cases vs. those of study controls. Except for OXT; Lack of Correlations between salivary and plasma levels of cardiometabolic risk biomarkers of pharmacotherapy ( Table 4 ). Exceptionally oxytocin, amongst 9 cardiometabolic risk biomarkers of pharmacotherapy studied, had proportional significant correlations between plasma and saliva levels, in both total sample and MetS patients (P value < 0.05) Correlations of plasma and salivary oxytocin with clinical and biochemical parameters, adiposity and atherogenecity indices ( Tables 5 – 6 ) Likewise, in the total sample plasma OXT (unlike salivary OXT) correlated significantly though inversely with both SBP and FBG. Collectively both blood and saliva OXT in the total study pool, as well as the remaining biomarkers; lacked comparably substantial associations with both adiposity and atherogenecity indices and clinical parameters of fasting lipid profile. Interestingly of MetS pool; markedly Proportional correlations of plasma (but not salivary) OXT with TG, and adiposity indices of LAP and VAI, and all atherogenecity indices were delineated. Discussion Lipocalin The MetS patients in our study had 17-folds increase in plasma lipocalin level compared to control; however, there was no significant difference in the salivary lipocalin levels between study groups. Circulating lipocalin-2 levels were previously shown to be higher in obese than in lean humans 47 and in MetS patients vs. control 11, 48 . Notably, no difference in salivary lipocalin-1 level was found between obese and normal weight individuals 49 . However, it is worth mentioning that, like irisin, lipocalin levels are associated with periodontitis and its severity 50 . We found lack of correlation between the plasma and salivary lipocalin level. None of the previous studies investigated such correlations in MetS patients. Nesfatin We found no difference in plasma nesfatin levels between the two study groups, like the results of the previous study conducted by our group 16 . However, data from Saudi Arabia showed that patients with MetS had significantly lower nesfatin levels compared to the control 17 . On the contrary, we showed elevated salivary nesfatin level in MetS patients in comparison with normal individuals. None of the previous studies investigated salivary nesfatin in MetS patients and its correlation with the plasma level. Omentin Neither plasma, nor salivary omentin were different between the MetS patients and the controls in our study. In our previous research we showed that in the Mets-pre/T2DM group, circulating levels of omentin-1were significantly lower vs. respective MetS-controls 51 . This is in contrast with the findings of decreased omentin-1 levels in pre-diabetic, T1DM, and newly diagnosed, untreated T2DM patients 12 as well as in morbidly obese women with MetS 19 . Decreased omentin-1 levels are generally associated with insulin resistance, diabetes, and metabolic syndrome, and atherosclerotic cardiovascular diseases. However, omentin-1 increases to counteract the acute phase after onset of these diseases 18 . Our study found no correlation between plasma and salivary omentin, in contrast to results of a very recent study that demonstrated correlation omentin in patients with periodontal disease and concomitant T2DM 20 . Oxytocin Neither serum, nor salivary oxytocin significantly differed between the MetS patient and controls in our study. We previously showed that oxytocin levels were significantly lower in both MetS groups (prediabetic and T2DM) than in MetS-normoglycemic subjects 4; 26–28 . However, this discrepancy may be explained by the differences in clinical and demographic characteristics between the studies (e.g., age of the control about 42 years in our study as opposed to about 29 years in 28 study). Qian et al. 52 also reported that serum oxytocin levels were decreased in obese adults as well as in adults with type 2 diabetes. Furthermore, Yuan et al. 53 demonstrated that patients with MetS had significantly lower oxytocin levels than did patients without MetS. In addition, a study in children demonstrated that oxytocin level is significantly lower in obese compared with non-obese patients and lower in obese patients with MetS compared to those without 23 . One of the most prominent results of our study is the positive correlation of oxytocin levels between plasma and saliva in both the total sample and the MetS patients. This agrees with the results by Hoffman et al. 29 in women with anorexia nervosa but contradicts the results of the studies in healthy volunteers of lack of correlation between plasma and salivary oxytocin 54–55 . Interestingly, saliva contamination by blood was shown to affect oxytocin detection in saliva by transferrin presence 56 . However, to the best of our knowledge, none of the previous studies investigated salivary-circulating oxytocin correlations in patients with MetS. As demonstrated in our previous study in the entire MetS study population (normoglycemic, prediabetic and T2DM patients), plasma oxytocin correlated negatively with HbA1c, FPG, resistin, adiponectin and leptin 28 . In the current study, plasma, but not salivary oxytocin correlated mainly with lipid parameters ad atherogenicity indices (TGs, LAP, non-HDL-C/HDL-C, VAI, LDL-C/HDL-C, TC/HDL-C, TG/HDL-C and AIP) in the MetS patients. Therefore, despite expectations, salivary OXT testing might not be a useful tool for non-invasive detection and assessment of MetS. RBP4 Our study found no difference in plasma or salivary RBP level between the MetS and the control, in agreement with our previous report that demonstrated lack of discrepancy in RBP4 circulating levels in both MetS groups (non-diabetic and preDM) vs. controls 33 . It was shown that the RBP-4 can be high or unchanged in glucose intolerance, type 2 diabetes, insulin resistance or metabolic syndrome 57 . RBP-4 concentrations were higher in patients with MetS than in controls 58 . In a large, population-based, cohort study the increased RBP4 serum levels were strongly associated with the presence and the number of components of MetS in a 65 + Caucasian population 31 . Besides, circulating RBP4 levels and RBP4 mRNA expression in visceral and subcutaneous abdominal adipose tissue are increased in obese patients compared with lean subjects 12 . It has been suggested that RPB4 concentrations may not be related necessarily to obesity itself, but to the location of the adipose tissue and are more closely associated with visceral fat levels, hence, appear to constitute the best indicator of intra-abdominal adipose mass 32 . In the Third Generation Cohort of the Framingham Heart Study, higher plasma RBP4 concentrations were not only associated with cross-sectional presence of MetS but also prospectively associated with incident MetS 59 . We found no correlation between the plasma and salivary RBP levels, and, to the best of our knowledge, none of the previous studies have assessed the salivary RBP levels in MetS patients. Resistin Our study shows that both plasma and salivary resistin did not differ between the MetS patients and the control. In our previous research, resistin levels were significantly higher in both MetS groups (prediabetic and T2DM) than in MetS-only subjects 28 , however, in a further study by our group that had study limbs similar to our current study design, the gradual increase in resistin levels, though not ascribed any statistically marked variation, was appreciable in both normoglycemic and preDM/MetS groups vs. controls 33 . Other studies showed that the circulating levels of resistin are upregulated in T2DM 5 . Our results are supported by that of a meta-analysis that showed no difference in the concentrations of resistin in saliva between individuals with and without obesity 60 . However, in another study, resistin concentrations were significantly higher in T2DM saliva 5 . There was a significant correlation between the salivary and serum resistin levels in healthy volunteers 8 but not in our whole sample or MetS only patients. It is worth noting, that none of the previous researchers investigated such correlations in MetS patients. SIRT 1 We found decreased plasma levels but increased salivary levels of SIRT 1 in patients with MetS when compared with the controls. This agrees with the results of our previous study where MetS patients had lower SIRT 1 level vs. controls 36 . We found no reports on salivary SIRT 1 in relation to MetS. This salivary biomarker was shown to be increased in patients with periodontal disease 61 . No correlation was detected between the plasma and the salivary SIRT 1 levels in our study. Visfatin We found that neither plasma, nor salivary visfatin differed between the MetS and the control groups. This contradicts the results of the studies which showed increased plasma visfatin in patients with overweight/obesity, T2DM, MetS and CVD 62–63 . In another study both the circulating and the salivary levels of visfatin were shown to be upregulated in T2DM 5 . The absence of correlation between the plasma and the salivary visfatin levels agrees with the study in healthy volunteers where no significant correlation between salivary and serum visfatin levels was observed 8 . ZBED3 There was no difference in plasma and salivary ZBED3 levels between the MetS patients and the controls in our study. We previously also found lack of difference in plasma ZBED3 level between MetS (both preDM and normoglycemic) and the controls 64 . However, in two studies from China, circulating Zbed3 levels were significantly higher in individuals with impaired glucose tolerance and newly diagnosed T2DM relative to those with normal glucose tolerance 65 and, similarly, in newly diagnosed MetS patients than in non-MetS subjects 66 . There might be an inter-ethnic difference in ZBED3 involvement in MetS patients. No correlation was found between the plasma and the salivary ZBED levels in the whole study sample and in the MetS group. To the best of our knowledge, there is no previous research on ZBED3 in saliva. Assessment of proteins from different functional classes is a plausible strategy to improve predictive ability, for example of T2DM. Notably, low-molecular-weight proteins (< 20 kDa) are more prevalent (14.5%) in the salivary proteome as compared to only 7% for the plasma proteome 67 . In a study 68 that involved individuals with T2DM, 65 salivary proteins demonstrated a greater than two-fold difference compared to control; a majority of the differentially abundant proteins belong to pathways regulating metabolism and immune response. Importantly, the study found a trend of relative increase in the salivary proteins abundance with progression from the pre-diabetic to the diabetic state. On the other hand, in a study of 27 different cytokines involving 50 healthy adults there was little correlation between the plasma and salivary samples; therefore, it was concluded that substituting saliva for blood needs a great caution, and that relationships differ by biomarker 69 . We support the opinion that the data of salivary biomarkers’ levels should be interpreted with caution as the type of sample (stimulated vs. unstimulated; whole vs. glandular), timing of sampling, sensitivity to preprocessing as well as presence of oral diseases are some of the confounding parameters may affect the biomarkers salivary levels 5 . Such cofounders include periodontitis, uneven salivary dilution level, or other exogenous factors 7 . Conclusions Among 9 cardiometabolic biomarkers, we found correlations between the plasma and the saliva for oxytocin. Salivary testing of oxytocin may be the promising noninvasive method of early detection/prediction/prevention/prognosis parameter of MetS/prediabetes. Abbreviations Adiposity indices (BMI, WHR (waist/Hip ratio), WtHR (waist/Height ratio), Conicity-index, BAI (Body adiposity index), LAP (Lipid accumulation Product), VAI (Visceral adiposity Index)) and atherogenicity indices (AIP (atherogenecity index of plasma) Declarations Ethics approval and consent to participate and publish were obtained and hence implemented Author contributions All authors contributed equally towards manuscript conceptualization, composition, statistical analysis and final draft approval Acknowledgements This study was funded by Deanship of Scientific Research/University of Jordan The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. Conflict of Interest : None References López-Jaramillo P, Gómez-Arbeláez D, López-López J, et al The role of leptin/adiponectin ratio in metabolic syndrome and diabetes. Hormone Mol Biol Clin Invest., 2014; 18(1):37–45. https://doi:10.1515/hmbci-2013-0053 Jialal I, Devaraj S Subcutaneous adipose tissue biology in metabolic syndrome. Hormone Mol Biol Clin Invest., 2018; 33(1). https://doi:10.1515/hmbci-2017-0074 . Ghadge AA, Khaire AA Leptin as a predictive marker for metabolic syndrome. Cytokine 2019; 121: 154735. https://doi:10.1016/j.cyto.2019.154735 . Kasabri V, Shawakri E, Akour A, et al Cross-sectional correlates of increased IL-18 but reduced fetuin-A and oxytocin with adiposity and blood indices in metabolic syndrome patients with and without prediabetes. Ther Adv Endocrinol Metab. 2018; 9(12):329–338. https://doi:10.1177/2042018818788802 . Srinivasan M, Meadows ML, Maxwell L Assessment of Salivary Adipokines Resistin, Visfatin, and Ghrelin as Type 2 Diabetes Mellitus Biomarkers. Biochem Res Int. , 2018; 2018: 7463796. https://doi:10.1155/2018/7463796 Loo JA, Yan W, Ramachandran P, Wong DT Comparative human salivary and plasma proteomes. J Dent Res., 2010; 89(10):1016–1023. https://doi:10.1177/0022034510380414 . Srinivasan M, Blackburn C, Mohamed M, et al Literature-based discovery of salivary biomarkers for type 2 diabetes mellitus. Biomarker Insights, 2015; 10:39–45. https://doi:10.4137/BMI.S22177 . Mamali I, Roupas ND, Armeni AK, et al Measurement of salivary resistin, visfatin and adiponectin levels. Peptides 2012; 33:120–4. http://doi:10.1016/j.peptides.2011.11.007 Brum RS, Duarte PM, Canto GL, et al Biomarkers in biological fluids in adults with periodontitis and/or obesity: A meta-analysis. J Ind Soc of Periodont., 2020; 24(3):191–215. https://doi:10.4103/jisp.jisp_512_19 . Tierney C, Bazou D, Lê G, Dowling P, O'Gorman P Saliva-omics in plasma cell disorders- Proof of concept and potential as a non-invasive tool for monitoring disease burden. J Proteomics. 2021; 231:104015. https://doi:10.1016/j.jprot.2020.104015 Jang Y, Lee JH, Wang Y, Sweeney G Emerging clinical and experimental evidence for the role of lipocalin-2 in metabolic syndrome. Clin Exp Pharmcol Physiol 2012; 39(2):194–199. http://doi:10.1111/j.1440-1681.2011 . 05557.x. Chen X, Jia X, Qiao J, et al Adipokines in reproductive function: a link between obesity and polycystic ovary syndrome. J Mol Endocrinol 2013; 50(2): R21–R37. http://doi:10.1530/JME-12-0247 Francisco V, Ruiz-Fernández C, Pino J, et al Adipokines: Linking metabolic syndrome, the immune system, and arthritic diseases. Biochem Pharmacol 2019; 165: 196–206. http://doi:10.1016/j.bcp.2019.03.030 . Luo JJ, Wen FJ, Qiu D, Wang F Nesfatin-1 in lipid metabolism and lipid-related diseases. Clin Chim Acta, 2021; 522: 23–30. https://doi.org/10.1016/j.cca.2021.08.005 . Tekin T, Cicek B, Konyaligil N Regulatory Peptide Nesfatin-1 and its Relationship with Metabolic Syndrome. Euras J Med 2019; 51(3): 280–284. http://doi:10.5152/eurasianjmed.2019.18420 . Al-Qudah SA, Kasabri V, Saleh MI, et al Cross-sectional correlates of nesfatin and lipopolysaccharide binding protein in metabolic syndrome patients with and without prediabetes. Hormone Mol Bio Clin Invest 2018; 36(3): 10.1515/hmbci-2018-0035 . http://doi:10.1515/hmbci-2018-0035. Alotibi MN, Alnoury AM, Alhozali AM Serum nesfatin-1 and galanin concentrations in the adult with metabolic syndrome. Relationships to insulin resistance and obesity. Saudi Med J., 2019; 40(1):19–25. http://doi:10.15537/smj.2019.1.22825 . Watanabe T, Watanabe-Kominato K, Takahashi Y, et al Adipose Tissue-Derived Omentin-1 Function and Regulation. Compr Physiol. 2017; 7(3):765–781. http://doi:10.1002/cphy.c160043 . Auguet T, Quintero Y, Riesco D, et al New adipokines vaspin and omentin. Circulating levels and gene expression in adipose tissue from morbidly obese women. BMC Med Gen 2011; 12:60. http://doi:10.1186/1471-2350-12-60 Dooxa Nongrum A, Guru SR, K J N, Aghanashini S. Analysing adipokine Omentin-1 in periodontal disease and type-2 diabetes mellitus: An interventional comparative study. J Oral Biol Craniofac Res. 2022; 12(2):273–278. http://doi:10.1016/j.jobcr.2022.03.010 . Erdenebayar O, Kato T, Kawakita T, et al Effects of peripheral oxytocin administration on body weight, food intake, adipocytes, and biochemical parameters in peri- and postmenopausal female rats. Endocr J. 2021; 68(1):7–16. http://doi:10.1507/endocrj.EJ19-0586 Camerino C The New Frontier in Oxytocin Physiology: The Oxytonic Contraction. Int J Mol Sci. 2020; 21(14):5144. http://doi:10.3390/ijms21145144 . Binay Ç, Paketçi C, Güzel S, Samancı N Serum Irisin and Oxytocin Levels as Predictors of Metabolic Parameters in Obese Children. J Clin Res Pediatr Endocrinol. 2017; 9(2):124–131. https://doi:10.4274/jcrpe.3963 . McCormack SE, Blevins JE, Lawson EA Metabolic Effects of Oxytocin. Endocr Rev. 2020; 41(2):121–45. http://doi:10.1210/endrev/bnz012 . Iwasa T, Matsuzaki T, Mayila Y, et al. The effects of chronic oxytocin administration on body weight and food intake in DHT-induced PCOS model rats. Gynecol Endocrinol. 2020; 36(1):55–60. http://doi:10.1080/09513590 . 2019.1631276. AL-Nouaaimi MM, Kasabri V, Akour A, et al Evaluation of the Correlation of Oxytocin Plasma Levels and Metabolic Syndrome Biomarkers (Leptin, Adiponectin and Resistin) in newly diagnosed Type 2 Diabetes Patients in Jordan: A Cross Sectional Study. Jordan J Pharm Sci 2016; 9(2):115–128. http://doi:10.12816/0033310 Al-Rawashdeh A, Kasabri V, Bulatova N, et al The correlation between plasma levels of oxytocin and betatrophin in non-diabetic and diabetic metabolic syndrome patients: A cross sectional study from Jordan. Diabetes Metab Syndr 2017; 11(1): 59–67. http://doi:10.1016/j.dsx.2016.08.008 . Akour A, Kasabri V, Bulatova N, et al. Association of Oxytocin with Glucose Intolerance and Inflammation Biomarkers in Metabolic Syndrome Patients with and without Prediabetes. Rev Diab Stud: RDS 2018; 14(4): 364–371. http://doi:10.1900/RDS.2017.14.364 . Hoffman ER, Brownley KA, Hamer RM, Bulik CM Plasma, salivary, and urinary oxytocin in anorexia nervosa: a pilot study. Eating Behaviors 2012; 13(3): 256–259. http://doi:10.1016/j.eatbeh.2012.02.004 . Ansar H, Mirzaei K, Malek A, et al Possible resting metabolic rate modification by the circulating RBP4 in obese subjects. Diabetes Metab Syndr 2015; 9(1): 19–23. http://doi:10.1016/j.dsx.2014.09.012 . Majerczyk M, Kocełak P, Choręza P, et al Components of metabolic syndrome in relation to plasma levels of retinol binding protein 4 (RBP4) in a cohort of people aged 65 years and older. J Endocr Invest 2018; 41(10):1211–1219. http://doi:10.1007/s40618-018-0856-6 . Rychter AM, Skrzypczak-Zielińska M, Zielińska A, et al Is the Retinol-Binding Protein 4 a Possible Risk Factor for Cardiovascular Diseases in Obesity?. Int J Mol Sci 2020; 21(15): 5229. http://doi:10.3390/ijms21155229 . Horan AA, Albsoul-Younes A, Kasabri V, et al Correlates of resistin and retinol-binding protein 4 in metabolic syndrome patients with and without prediabetes. Horm Mol Biol Clin Investig. 2019; 37(3): /j/hmbci.2019.37.issue-3/hmbci-2018-0051/hmbci-2018-0051.xml . Kane AE, Sinclair DA Sirtuins and NAD + in the Development and Treatment of Metabolic and Cardiovascular Diseases. Circ Res. 2018; 123(7):868–885. http://doi:10.1161/CIRCRESAHA.118.312498 . Lipphardt M, Song JW, Goligorsky MS Sirtuin 1 and endothelial glycocalyx. Pflugers Arch. 2020; 472(8): 991–1002. http://doi:10.1007/s00424-020-02407-z . Rkhaya SA, Bulatova N, Kasabri V, et al Increased malondialdehyde vs. reduced sirtuin 1 in relation with adiposity, atherogenicity and hematological indices in metabolic syndrome patients with and without prediabetes. Diabetes Metab Syndr. 2018; 12(6):903–909. http://doi:10.1016/j.dsx.2018.05.013 . Fantuzzi G Adipose tissue, adipokines, and inflammation. J Allergy Clin Immunol. 2005; 115:911–9. http://doi:10.1016/j.jaci.2005.02.023 Chen T, Li M, Ding Y, et al Identification of zinc-finger BED domain-containing 3 (Zbed3) as a novel Axin-interacting protein that activates Wnt/beta-catenin signaling. J Biol Chem. 2009; 284(11):6683–9. https://doi:10.1074/jbc.M807753200 . Jia, Y, Yuan, L, Hu, W, et al Zinc-finger BED domain-containing 3 (Zbed3) is a novel secreted protein associated with insulin resistance in humans. J Intern Med 2014; 275: 522–533. http://doi:10.1111/joim.12170 . International diabetes Federation (IDF). Worldwide definition of the metabolic syndrome. The IDF consensus worldwide definition of the Metabolic Syndrome 2006:1e19. https:// American Diabetes Association; 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2021. Diabetes Care . 2021; 44 (S1): S15–S33. https://doi:10.2337/dc21-S002 . Snouper A, Kasabri V, Bulatova N, Suyagh M, et al. Plasma Carnitine, Choline, γ-Butyrobetaine, and Trimethylamine N-oxide, but not Zonulin, are reduced in overweight/obese Patients with Pre/diabetes or Impaired Glycemia. Int J Diabetes Develop Countr.; 2022. https://doi.org/10.1007/s13410-022-01088-x Bergman RN, Stefanovski D, Buchanan TA, et al A better index of body adiposity. Obesity (Silver Spring, Md.) 2013; 19(5): 1083–1089. https://doi:10.1038/oby.2011.38 . Dobiášová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FERHDL). Clin Biochem.; 2001; 34:583–8 https://doi:10.1016/s0009-9120(01)00263-6 . (a)Alberti KGMM, Eckel RH, Grundy SM et al Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation , 2009; 120(16):1640-5. https://doi:10.1161/CIRCULATIONAHA.109.192644 ; (b) Al-Batsh MMW, Albsoul-Younes A, Kasabri V, et al. Proportional correlates of cystatin-C with pentraxin-3, visceral adiposity index and atherogenicity index of plasma but not blood indices in metabolic syndrome patients with and without prediabetes. Horm Mol Biol Clin Investig . 2018; 36(3). doi: 10.1515/hmbci-2018-0058. Chan YH. Biostatistics 104: Correlational analysis. Singapore Med J. 2003; 44(12):614–619. https://PMID: 1477 0254 Wang Y, Lam KS, Kraegen EW et al Lipocalin-2 is an inflammatory marker closely associated with obesity, insulin resistance, and hyperglycemia in humans. Clin Chem 2007; 53: 34–41. http://doi:10.1373/clinchem.2006.075614 Ni J, Ma X, Zhou M, et al Serum lipocalin-2 levels positively correlate with coronary artery disease and metabolic syndrome. Cardiovasc Diabetol. 2013; 12:176. http://doi:10.1186/1475-2840-12-176 . Vors C, Drai J, Gabert L, et al Salivary composition in obese vs. normal-weight subjects: towards a role in postprandial lipid metabolism?. Int J Obes 2015; 39(9):1425–1428. http://doi:10.1038/ijo.2015.71 Tan A, Gürbüz N, Özbalci Fİ, et al Increase in serum and salivary neutrophil gelatinase-associated lipocalin levels with increased periodontal inflammation. J Appl Oral Sci: Revista FOB 2020; 28:e20200276. http://doi:10.1590/1678-7757-2020-0276 . Kahwaji R, Kasabri V, Bulatova N et al. Evaluation of correlations of Plasma Levels of Oxytocin, Omentin-1 and Irisin in Diabetic and Non-Diabetic Metabolic Syndrome Patients: A Cross Sectional Study in Jordan. Jordan Med J., 2017; 51(3): 97–810 Qian W, Zhu T, Tang B, et al Decreased circulating levels of oxytocin in obesity and newly diagnosed type 2 diabetic patients. J Clin Endocrinol Metab 2014;99:4683–4689. http://doi:10.1210/jc.2014-2206 . Yuan G, Qian W, Pan R, et al Reduced circulating oxytocin and High-Molecular-Weight adiponectin are risk factors for metabolic syndrome. Endocr J 2016; 63:655–662. http://doi:10.1507/endocrj.EJ16-0078 . Javor A, Riedl R, Kindermann H, et al Correlation of plasma and salivary oxytocin in healthy young men - experimental evidence. Neuro Endocrinol Lett . 2014; 35(6):470-3. https://PMID: 25433841 Martins D, Gabay AS, Mehta M, Paloyelis Y Salivary and plasmatic oxytocin are not reliable trait markers of the physiology of the oxytocin system in humans. Elife. 2020; 9:e62456. http://doi:10.7554/eLife.62456 . Behr GA, Patel JP, Coote M, et al A statistical method to calculate blood contamination in the measurement of salivary hormones in healthy women. Clin Biochem . 2017; 50(7–8):436–439. http://doi:10.1016/j.clinbiochem . 2016.12.005. Karakas SE, Banaszewska B, Spaczynski RZ, et al Free fatty acid binding protein-4 and retinol binding protein-4 in polycystic ovary syndrome: response to simvastatin and metformin therapies. Gyn Endocr 2013; 29(5), 483–487. http://doi:10.3109/09513590.2013.774360 . Tabak O, Simsek G, Erdenen F et al The relationship between circulating irisin, retinol binding protein-4, adiponectin and inflammatory mediators in patients with metabolic syndrome. Arch Endocrinol Metab, 2017; 61(6):515–523. http://doi:10.1590/2359-3997000000289 . Zachariah JP, Quiroz R, Nelson KP, et al Prospective Relation of Circulating Adipokines to Incident Metabolic Syndrome: The Framingham Heart Study. J Am Heart Assoc. 2017; 6(7): e004974. http://doi:10.1161/JAHA. 116.004974 . Duffles LF, Hermont AP, Abreu LG, et al Association between obesity and adipokines levels in saliva and gingival crevicular fluid: A systematic review and meta-analysis. J Evid-Based Med., 2019; 12(4):313–324. http://doi:10.1111/jebm.12363 . Kluknavská J, Krajčíková K, Bolerázska B Possible prognostic biomarkers of periodontitis in saliva. Eur Rev Med Pharmacol Sci. 2021; 25(8):3154–3161. http://doi:10.26355/eurrev_202104_25724 . Chang Y-H, Chang D-M, Lin K-C, et al Visfatin in overweight/obesity, type 2 diabetes mellitus, insulin resistance, metabolic syndrome and cardiovascular diseases: a meta-analysis and systemic review. Diabetes/Metab Res Rev 2011; 27:515–527. http://doi:10.1002/dmrr.1201 . Bayani M, Pourali M, Keivan M Possible interaction between visfatin, periodontal infection, and other systemic diseases: A brief review of literature. Eur J Dent. 2017; 11(3):407–410. http://doi:10.4103/ejd.ejd_284_16 . AbuZayed R, Bulatova N, Kasabri V, et al. Correlates of zinc finger BED domain-containing protein 3 and ghrelin in metabolic syndrome patients with and without prediabetes. Horm Mol Biol Clin Investig. 2019; 37(3): /j/hmbci.2019.37.issue-3/hmbci-2018-0052/hmbci-2018-0052.xml . Jia Y, Yuan L, Hu W et al Zinc-finger BED domain-containing 3 (Zbed3) is a novel secreted protein associated with insulin resistance in humans. J Intern Med. 2014; 275(5):522–33. http://doi : 10.1111/joim.12170 . Hu W, Tian B, Li X, et al Circulating Zbed3 Levels in Subjects With and Without Metabolic Syndrome. Metab Syndr Relat Disord 2017; 15(5):207–212. http://doi:10.1089/met.2016.0122 . Loo JA, Yan W, Ramachandran P, Wong DT Comparative human salivary and plasma proteomes. J Dent Res 2010; 89(10):1016–1023. http://doi:10.1177/0022034510380414 . Rao PV, Reddy AP, Lu X et al Proteomic identification of salivary biomarkers of type-2 diabetes. J Proteom Res., 2009; 8(1):239–245. http://doi:10.1021/pr8003776 . Williamson S, Munro C, Pickler R, et al Comparison of biomarkers in blood and saliva in healthy adults. Nurs Res Pract. , 2012; 2012:246178. http://doi:10.1155/2012/246178 . Tables Table 1. IDF metabolic syndrome (MetS) world-wide definition Raised triglycerides ≥ 1.7 mmol/l (150 mg/dL)or specific treatment for this lipid abnormality Reduced HDL- cholesterol < 1.03 mmol/l (40 mg/dL) in males < 1.29 mmol/l (50 mg/dL) in females or specific treatment for this lipid abnormality Raised blood pressure Systolic: ≥ 130 mmHg Or Diastolic: ≥ 85 mmHg or treatment of previously diagnosed hypertension Raised fasting plasma glucose Fasting plasma glucose ≥ 5.6 mmol/l (100 mg/dL) or previously diagnosed Type 2 diabetes If > 5.6 mmol/l or 100 mg/dL, oral glucose tolerance test is strongly recommended but is not necessary to define presence of the syndrome *For Eastern Mediterranian and Middle East polulation, the measure of central obesity include waist circumference of ≥ 94 cm for males and ≥ 80 cm in females. N.B.: If body mass index is > 30 kg/m 2 then central obesity can be assumed, and waist circumference does not need to be measured. Table 2. Comparison of demographic, clinical laboratory parameters, adiposity and atherogenicity indices between the study groups Characteristic Total sample (N=92), N (%) Controls (N=31), N (%) MetS (N=61), N (%) P Demographic and clinical characteristics Gender Female Male 68 (73.9) 24 (77.4) 44 (72.1) 0.585 # 24(26.1) 7 (22.6) 17 (27.9) Diabetes status among MetS patients Prediabetes 26 (42.6) Diabetes 5 (8.2) Normoglycemic 30 (49.2) Total sample (N=92), Mean (SD) or Median [interquartile range] Control (N=31), Mean (SD) or Median [interquartile range] MetS (N=61), Mean (SD) or Median [interquartile range] P Age (years) 48.62 (11.21) 43.29 (11.72) 51.11 (10.16) 0.001 SBP (mm Hg) 131.98 (15.21) 116.62 (10.22) 139.16 (11.35) <0.001 DBP (mm Hg) 81.88 (11.50) 72.38 (10.18) 86.31 (9.22) <0.001 FPG (mg/dL) 91.90 [19.00] 86.46 (7.44) 96.70 [18.00] <0.001^ A1c (%) 5.40 [0.8] 5.10 [0.50] 5.60 [0.70] <0.001^ TG (mg/dL) 152.00 [141] 95.16 (30.54) 171.00 [125.00] <0.001^ LDL-C (mg/dL) 138.41 (39.22) 128.05 (28.01) 143.25 (39.25) 0.094 HDL-C (mg/dL) 49.51 (15.93) 57.21 (112.37) 45.92 (16.24) <0.001 Non-HDL-C (mg/mL) 159.03 (44.17) 147.00 [55.00] 169.49 (41.03) <0.001^ TC (mg/dL) 208.54 (45.41) 193.23 (40.40) 215.41 (46.39) 0.054 Adiposity indices BMI (kg/m 2 ) 29.32 [9.99] 23.53 (1.67) 30.37 [10.84] <0.001^ WHR 0.91 (0.06) 0.88 (0.07) 0.92 (0.05) 0.002 WHtR 0.62 (0.09) 0.54 (0.05) 0.66 (0.08) <0.001 C-index 1.33 [0.13] 1.30 (0.11) 1.33 [0.09] 0.01^ BAI 35.37 [12.48] 30.10 (4.76) 39.30 (8.70) <0.001 LAP 61.28 [75.28] 24.29 [23.84] 100.67 [81.47] <0.001^ VAI 2.35 [2.38] 1.36 (0.56) 3.15 [3.05] <0.001^ Atherogenicity indices AIP 0.49 [0.51] 0.22(0.22) 0.59 [0.39] <0.001^ TC/HDL-C 4.31 [1.82] 3.08 [1.76] 4.71 [1.90] <0.001^ LDL-C/HDL-C 3.01 [1.42] 2.48 (1.05) 3.14 [1.47] <0.001^ Non-HDL-C/HDL-C 3.85 (4.44) 2.57 (1.18) 3.71 [1.91] <0.001^ TG/HDL-C 3.11 [3.87] 1.86 (0.88) 3.91 [3.84] <0.001^ Abbreviations: AIP – atherogenic index of plasma; A1c – glycated hemoglobin; BAI - body adiposity index; BMI – body mass index; C-index - conicity index; DBP – diastolic blood pressure; FPG – fasting plasma glucose; HDL- C – high-density lipoprotein-cholesterol; LAP – lipid accumulation product; LDL-C – low-density lipoprotein-cholesterol; MetS – metabolic syndrome; non-HDL-C – non—high-density lipoprotein-cholesterol; SBP –systolic blood pressure; TC – total cholesterol; TG – triglycerides; WHR - waist to hip ratio ; WHtR - waist-to-height ratio; * Normally distributed data are presented as means (SD), not normally distributed data are presented as median [interquartile range]. #Comparison between MetS and control was made by Chi square. ^ Comparison between MetS and Control was made by Mann-Whitney test. Table 3. Comparison of plasma and salivary cardiometabolic risk biomarkers of pharmacotherapy between the study groups Biomarker Total sample (N=92), Mean (SD) or Median [interquartile range] Control (N=31), Mean (SD) or Median [interquartile range] MetS (N=61), Mean (SD) or Median [interquartile range] P value p.Lipocalin (ng/mL) 377700.00 [187750.00] 23375.05 (19902.87) 397400.00 [195200.00] 0.019^ s.Lipocalin (ng/mL) 23480.00 [32565.00] 21980.00 [31050.00] 26780.00 [38680] 0.479^ p.Nesfatin (pg/mL) 506.00 [4286.00] 546.00 [2254.00] 482.00 [219.00 0.144^ s.Nesfatin (pg/mL) 136.00 [21.00] 128.95 (12.82) 142.00 [25] 0.011^ p.Omentin (ng/mL) 22.73 [22.14] 18.73 [16.18] 24.18 [32.91] 0.157^ s.Omentin (ng/mL) 284.92 [129.58] 305.83 [134.08] 281.67 [139.08] 0.370^ p.OXT (pg/mL) 1391.91 [849.00] 1565.36 [671.12] 1284.76 [923.52] 0.081^ s.OXT (pg/mL) 101.00 [92.4] 96.11 [128.70] 104.63 [202.47] 0.595^ p.RBP4 (ng/mL) 28266.67 [3800.00] 29082.53 (2287.83) 27800.00 [3466.67] 0.122^ s.RBP4 (ng/mL) 237.50 [128.00] 251.00 [121] 213.60 (88.54) 0.989^ p.Resistin (ng/mL) 24.01 [19.40] 23.33 [19.35] 25.10 [19.17] 0.229^ s.Resistin (ng/mL) 29.57 [32.00] 32.32 (18.76) 28.51 [32.00] 0.667^ p.SIRT 1 (ng/mL) 1.78 [2.10] 3.88 (0.79) 1.40 [0.80] <0.001^ s.SIRT 1 (ng/mL) 16.81 [18.07] 14.15 (9.29) 19.50 (12.01) 0.029 p.Visfatin (ng/mL) 29.31 [50.31] 34.00 [57.75] 28.13 [53.06] 0.680^ s.Visfatin (ng/mL) 433.38 [141.10 402.50 [133.00] 448.25 [156.3] 0.746^ p.ZBED3 (ng/mL) 0.25 [0.29] 0.19 [0.17] 0.28 [0.34] 0.059^ s.ZBED3 (ng/mL) 0.26 [0.25] 0.28 (0.23) 0.26 [0.20] 0.401^ Abbreviations: OXT – oxytocin; p - plasma; RBP – retinol binding protein; s - salivary; SIRT 1 - sirtuin 1; ZBED - zinc finger, BED-type. * Normally distributed data are presented as means (SD), not normally distributed data are presented as median [interquartile range]. #Comparison between MetS and control was made by Chi square. ^ Comparison between MetS and Control was made by Mann-Whitney test. Table 4. Except for OXT plasma and salivary concentrations; Lack of Correlations between plasma and salivary concentrations of cardiometabolic risk biomarkers of pharmacotherapy Marker Spearman's correlation Total sample MetS patients Lipocalin (ng/mL) Correlation Coefficient 0.130 0.02 Sig. (2-tailed) 0.231 0.88 N 86 58 Nesfatin (pg/mL) Correlation Coefficient -0.071 -0.018 Sig. (2-tailed) 0.521 0.891 N 84 57 Omentin (ng/mL) Correlation Coefficient 0.003 0.096 Sig. (2-tailed) 0.977 0.476 N 85 57 OXT (pg/mL) Correlation Coefficient 0.259 * 0.307* Sig. (2-tailed) 0.020 0.027 N 81 52 RBP4 (ng/mL) Correlation Coefficient 0.139 0.138 Sig. (2-tailed) 0.203 0.297 N 86 59 Resistin (ng/mL) Correlation Coefficient 0.209 0.248 Sig. (2-tailed) 0.053 0.058 N 86 59 SIRT1 (ng/mL) Correlation Coefficient -0.072 0.117 Sig. (2-tailed) 0.514 0.386 N 84 57 Visfatin (ng/mL) Correlation Coefficient 0.047 0.042 Sig. (2-tailed) 0.672 0.755 N 85 57 ZBED3 (ng/mL) Correlation Coefficient -0.181 -0.108 Sig. (2-tailed) 0.099 0.424 N 84 57 Abbreviations: OXT – oxytocin; RBP – retinol binding protein; SIRT 1 - sirtuin 1; VAI – visceral adiposity index; ZBED - zinc finger, BED-type. Table 5a. Correlations of plasma and salivary oxytocin with clinical and demographic parameters and adiposity indices in the total sample Age C-index BMI BAI WHR WHtR SBP DBP FBG A1C TG p.OXT (pg/mL) Correlation Coefficient -0.078 -0.125 -0.153 -0.048 -0.053 -0.149 -0.250 * -0.119 -0.280 ** -0.202 0.077 Sig. (2-tailed) 0.476 0.256 0.163 0.663 0.631 0.173 0.021 0.278 0.010 0.064 0.485 N 85 85 85 85 85 85 85 85 85 85 85 s.OXT (pg/mL) Correlation Coefficient 0.020 -0.118 -0.025 0.000 0.058 -0.011 0.146 0.151 -0.187 -0.111 -0.046 Sig. (2-tailed) 0.859 0.295 0.825 1.000 0.604 0.920 0.193 0.179 0.095 0.326 0.682 N 81 81 81 81 81 81 81 81 81 81 81 Table 5b. Lack of Correlations of plasma and salivary oxytocin with atherogenecity indices in the total sample (continued) LAP LDL-Cl HDL-C TC Non-HDL-C Non HDL-C/HDL-C VAI TC/HDL-C LDL-C/HDL-C TG/HDL-C AIP p.OXT (pg/mL) Correlation Coefficient 0.000 -0.009 -0.083 0.045 0.054 0.096 0.092 0.105 0.094 0.063 0.063 Sig. (2-tailed) 0.997 0.937 0.452 0.681 0.622 0.383 0.405 0.339 0.391 0.568 0.568 N 85 85 85 85 85 85 85 85 85 85 85 s.OXT (pg/mL) Correlation Coefficient -0.049 0.087 0.155 0.103 0.037 -0.080 -0.070 -0.090 0.013 -0.089 -0.089 Sig. (2-tailed) 0.663 0.438 0.166 0.360 0.743 0.480 0.533 0.423 0.906 0.427 0.427 N 81 81 81 81 81 81 81 81 81 81 81 Abbreviations: AIP – atherogenic index of plasma; A1c – glycated hemoglobin; BAI - body adiposity index; BMI – body mass index; C-index - conicity index; DBP – diastolic blood pressure; FPG – fasting plasma glucose; HDL- C – high-density lipoprotein-cholesterol; LAP – lipid accumulation product; LDL-C – low-density lipoprotein-cholesterol; MetS – metabolic syndrome; non-HDL-C – non—high-density lipoprotein-cholesterol; OXT – oxytocin; p - plasma; s - salivary; SBP – systolic blood pressure; TC – total cholesterol; TG – triglycerides; VAI – visceral adiposity index; WHR - waist to hip ratio -; WHtR - waist-to-height ratio Table 6 a. Correlations of plasma and salivary oxytocin with clinical and demographic parameters and adiposity indices in MetS patients Age C-index BMI BAI WHR WHtR SBP DBP FBG A1C TG p.OXT (pg/mL) Correlation Coefficient -0.047 0.024 -0.042 0.045 0.017 0.000 -0.193 -0.062 -0.157 -0.211 0.317 * Sig. (2-tailed) 0.734 0.863 0.761 0.745 0.903 0.998 0.154 0.651 0.248 0.119 0.017 N 56 56 56 56 56 56 56 56 56 56 56 s.OXT (pg/mL) Correlation Coefficient -0.090 0.041 -0.143 -0.108 0.045 -0.061 0.197 0.242 -0.244 -0.176 -0.145 Sig. (2-tailed) 0.526 0.772 0.312 0.448 0.749 0.666 0.162 0.084 0.081 0.212 0.304 N 52 52 52 52 52 52 52 52 52 52 52 Table 6b. Correlations of plasma and salivary oxytocin with atherogenecity indices in MetS patients (continued) LAP LDL-C HDL-C TC Non HDL-C Non HDL-C/HDL-C VAI TC/HDL-C LDL-C/HDL-C TG/HDL-C AIP p.OXT (pg/mL) Correlation Coefficient 0.305 * 0.037 -0.251 0.144 0.196 0.318 * 0.351 ** 0.319 * 0.277 * 0.307 * 0.307 * Sig. (2-tailed) 0.022 0.784 0.062 0.290 0.148 0.017 0.008 0.017 0.038 0.022 0.022 N 56 56 56 56 56 56 56 56 56 56 56 s.OXT (pg/mL) Correlation Coefficient -0.140 0.209 0.121 0.148 0.121 -0.016 -0.145 -0.019 0.104 -0.178 -0.178 Sig. (2-tailed) 0.323 0.137 0.394 0.294 0.394 0.910 0.304 0.896 0.461 0.206 0.206 N 52 52 52 52 52 52 52 52 52 52 52 Abbreviations: AIP – atherogenic index of plasma; A1c – glycated hemoglobin; BAI - body adiposity index; BMI – body mass index; C-index - conicity index; DBP – diastolic blood pressure; FPG – fasting plasma glucose; HDL- C – high-density lipoprotein-cholesterol; LAP – lipid accumulation product; LDL-C – low-density lipoprotein-cholesterol; MetS – metabolic syndrome; non-HDL-C – non—high-density lipoprotein-cholesterol; OXT – oxytocin; p - plasma; s - salivary; SBP – systolic blood pressure; TC – total cholesterol; TG – triglycerides; VAI – visceral adiposity index; WHR - waist to hip ratio -; WHtR - waist-to-height ratio Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2587738","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":181207658,"identity":"cda81fbf-522c-43bc-ac94-a60dd5edda29","order_by":0,"name":"Nailya R. Bulatova","email":"","orcid":"","institution":"University of Jordan","correspondingAuthor":false,"prefix":"","firstName":"Nailya","middleName":"R.","lastName":"Bulatova","suffix":""},{"id":181207661,"identity":"fa695560-0e94-452f-bcc4-30213c03c7f0","order_by":1,"name":"Violet N. Kasabri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIie3PsQrCMBCA4ZNCugSyRhCf4UYHH+ZAyFTBSdwUBDuouNa36CNECrroKBi6OOni4tahg6k4ONW4CeYfAgf3kQTA5/vBMIOGBqDxLJxUY8eJgAaixorrapQORL/IOqFqdiDNOND6XqgAzfWYFgMJIp5TPZky2iQUMcyjoVnYh8n9If1AOGacRtwSdeKWoOzXExFYUtJIotkrUzoToAibSbjNnW5hgf3LQikSPGJ5CyX/+Bcmss256PaIhbuLuZXdtoiX9eQtjs/Tdb0qPH+z7fP5fH/UA5lBSYdcv/fkAAAAAElFTkSuQmCC","orcid":"","institution":"University of Jordan","correspondingAuthor":true,"prefix":"","firstName":"Violet","middleName":"N.","lastName":"Kasabri","suffix":""},{"id":181207663,"identity":"8d4a0f3a-15d6-4288-a38a-ab1ca0ec8e65","order_by":2,"name":"Abla M. Albsoul","email":"","orcid":"","institution":"University of Jordan","correspondingAuthor":false,"prefix":"","firstName":"Abla","middleName":"M.","lastName":"Albsoul","suffix":""},{"id":181207664,"identity":"a8674da8-8f72-4c0d-9649-e626d15c815c","order_by":3,"name":"Lana Halaseh","email":"","orcid":"","institution":"University of Jordan","correspondingAuthor":false,"prefix":"","firstName":"Lana","middleName":"","lastName":"Halaseh","suffix":""},{"id":181207665,"identity":"2ff43b80-27fc-4758-84d0-e3d6720e4473","order_by":4,"name":"Maysa Suyagh","email":"","orcid":"","institution":"University of Jordan","correspondingAuthor":false,"prefix":"","firstName":"Maysa","middleName":"","lastName":"Suyagh","suffix":""}],"badges":[],"createdAt":"2023-02-14 19:14:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2587738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2587738/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34193786,"identity":"c1cc46d3-834b-4132-950a-2a19b7b3eaf6","added_by":"auto","created_at":"2023-03-13 22:48:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRecruitment Flow Chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2587738/v1/51505ccfb8519e6cf1e8a1d2.jpg"},{"id":41755720,"identity":"832359ab-6766-4374-a1df-07f141da8099","added_by":"auto","created_at":"2023-08-18 09:52:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1121615,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2587738/v1/49f9e1ed-6239-44fa-83f6-a0380cceecdf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Clinical Utility of salivary oxytocin as a putatively surrogate early Risk Identification biomarker of nascent Metabolic Syndrome with and without prediabetes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe metabolic syndrome (MetS) is a constellation of cardiometabolic risk factors, adiposity and insulin resistance being its main features. The presence of MetS increases risk of coronary heart disease and type 2 diabetes mellitus (T2DM)\u003csup\u003e1\u003c/sup\u003e. Although the pathogenic mechanisms of MetS have not been elucidated, both increased inflammation and insulin resistance play a pivotal role. It seems that the monocyte/macrophages and adipose tissue (AT) interact to accentuate both the pro-inflammatory state and increased insulin resistance. There is a marked increase in macrophages and crown-like structures in the subcutaneous adipose tissue (SAT) of patients with MetS\u003csup\u003e2\u003c/sup\u003e. Adipose tissue production of cytokines (adipokines or adipocytokines) plays central role in MetS and T2DM\u003csup\u003e1\u003c/sup\u003e. Adipokines play a critical role in storage, food intake, energy expenditure, and lipid and glucose metabolism\u003csup\u003e3\u003c/sup\u003e. In MetS there is an increase in plasma leptin, plasminogen activator inhibitor-1, retinol-binding protein-4(RBP-4), chemerin, serum amyloid-A, C-reactive protein (CRP), interleukin-1, -6, -8, lipopolysaccharide, fetuin A (FetA) and a decrease in adiponectin and omentin-\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe growing evidence shows that the processes resulting in T2DM are started very early with a long lag phase between the disease onset and the clinical diagnosis. Multiple researches have evaluated various serum biomarkers as predictive for T2DM\u003csup\u003e4\u0026ndash;5\u003c/sup\u003e. Nearly 40% of the proteins that have been suggested to be candidate markers for diseases such as cancer, cardiovascular disease, and stroke can be found in whole saliva. With numerous plasma biomarkers verified for metabolic risk, relatively few studies have been carried in the area of salivary biomarkers. It has been observed that approximately 40% of cancer, stroke and cardiovascular disease biomarkers are present in whole saliva\u003csup\u003e6\u003c/sup\u003e.Finally it should be noted that 27% of the salivary proteome overlapped with the plasma proteome\u003csup\u003e6\u003c/sup\u003e. Human saliva is a rich reservoir of biomarkers including over 3652 proteins and 12,562 peptides and shares nearly 51% of proteins with the serum proteome and 79% of peptides with the serum peptidome\u003csup\u003e5\u003c/sup\u003e. Periodic evaluation of select plasma protein biomarkers during time course of type 2 diabetes mellitus (T2DM) development may increase the predictive ability of diabetes risk scores\u003csup\u003e7\u003c/sup\u003e. The use of salivary biomarkers has increasing value as a result of discovery of significant similarities between the salivary and serum proteomes\u003csup\u003e7\u003c/sup\u003e. The collection of saliva is a noninvasive, easily repeatable and less stressful technique than blood withdrawal. As an example, the levels of salivary resistin, visfatin, and adiponectin correlated with serum hormonal levels\u003csup\u003e8\u003c/sup\u003e. As an example, a recent meta-analysis of biomarkers in periodontitis and/or obesity demonstrated that, obesity and periodontitis, together or separately, are associated with altered serum and gingival crevicular fluid levels of leptin, adiponectin, and resistin, but concluded that the role of vaspin, omentin-1 and some other molecules, which can be key points underlying the association between obesity and periodontitis, remains to be further investigated\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this research we focused on adipose- and/or skeletal muscle-derived signaling as examples of metabotrophic factors (MTFs) involved in the pathogenesis of obesity and related cardiometabolic diseases\u003csup\u003e9\u003c/sup\u003e. Hence collectively a battery of proportionally associated with insulin resistance related adiposity and cardiometabolic risk factors. Hence it was the aim of this study to examine the early cardiometabolic risk associations of these peptides; namely Lipocalin, Nesfatin, Omentin, Oxytocin, RBP-4 (retinol-binding protein-4), Resistin, SIRT 1 (sirtuin 1), Visfatin and ZBED3 (zinc finger, BED-type (ZBED) protein 3) with adiposity \u0026ndash; and atherogenecity \u0026ndash;related insulin resistance in normoglycemic and dysglycemic metabolic syndrome population. Scarcity of studies that investigated correlations between plasma and salivary cardiomatabolic biomarkers\u0026rsquo; levels in MetS patients is clearly noticeable. Moreover given that saliva biomarkers seem to be promising in the area of metabolic syndrome (MetS) detection and diagnosis due to less invasive nature, less expensive and faster sample collection in comparison to plasma biomarkers\u003csup\u003e10\u003c/sup\u003e. Taken together it was this study aim to investigate the potential correlations between plasma and saliva levels of these cardiometabolic risk biomarkers for pharmacotherapy institution and follow up reasons of MetS patients with a defined cluster of adiposity and atherogenecity indices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLipocalin\u003c/strong\u003e 2 is a 25 kDa glycoprotein expressed in several cells as in adipocytes transporting small lipophilic ligands, as in lipopolysaccharides, through hydrophilic body fluid\u003csup\u003e11\u0026ndash;12\u003c/sup\u003e. It can be a pro-inflammatory factor elevated in obese \u0026frasl; inflammatory states\u003csup\u003e11\u003c/sup\u003e secreted by White adipose tissue (WAT).\u003csup\u003e13\u003c/sup\u003e In addition lipocalin 2 is involved in apoptosis, ion transport, inflammation, cell survival, tumorigenesis, reproduction and atherosclerosis\u003csup\u003e12\u003c/sup\u003e. \u003cstrong\u003eNesfatin is\u003c/strong\u003e an anorexic neuropeptide of significant regulation of energy metabolism and food intake and widely distributed in the central nervous system and peripheral tissues. It is linked to regulation of food intake and lipid metabolism, inhibiting fat accumulation, accelerating lipid decomposition, and in inhibiting the development of lipid-related disorders of obesity and metabolic syndrome (MetS)\u003csup\u003e14\u003c/sup\u003e. Furthermore it influences cardiac functions \u0026ndash;related glycemia and generates weight loss\u003csup\u003e15\u003c/sup\u003e. Lower levels of nesfatin (pg/mL) were reported in both pre-diabetic and non-diabetic MetS patients\u003csup\u003e16\u0026ndash;17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOmentin\u003c/strong\u003e is 313 amino acids-adipocytokine expressed by a diversity of tissues (as in mesothelial cells, vascular cells, airway goblet cells, omental and epicardial fat, small intestine, colon, ovary, and plasma). It sustains body metabolism and insulin sensitivity, with antiinflammatory, anti-atherosclerotic, and cardiovascular protective effects via AMP-activated protein kinase/Akt/nuclear factor-\u0026kappa;B/mitogen-activated protein kinase (ERK, JNK, and p38) signaling.\u003csup\u003e18\u003c/sup\u003e In a close-relation with T2DM;\u003csup\u003e12\u003c/sup\u003e this visceral adipokine expression in pre)adipocytes is decreased by glucose/insulin and stimulated by fibroblast growth factor-21 and dexamethasone. It can also enhance insulin-mediated glucose uptake in human subcutaneous and omental adipocytes\u003csup\u003e19\u003c/sup\u003e. It was reported to increase insulin sensitivity by activating Akt and enhancing insulin-(but not basal) stimulated glucose uptake both by subcutaneous and omental adipocytes; due to lack of intrinsic insulin-mimetic activity\u003csup\u003e12\u003c/sup\u003e. Significantly salivary and serum Omentin-1 were found related in chronic periodontitis and T2DM\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOxytocin (OXT)\u003c/strong\u003e is involved in the maintenance of labor and lactation in female reproduction. It has substantial roles in regulating social memory and anxiety\u003csup\u003e21\u003c/sup\u003e. \u003cstrong\u003eIt\u003c/strong\u003e is principally a nanopeptide released by paraventricular nucleus (PVN), and the supraoptic nucleus (SON) in the hypothalamus. With pronounced regulatory roles in energy homeostasis as an anorexigenic factor; low OXT circulating concentrations were found in diet-induced or genetically modified animal models of obesity and in humans.\u003csup\u003e22\u0026ndash;23\u003c/sup\u003e In amenorrheic athletes; OXT secretion proportionally correlated with measures of energy availability in linkage to weight and body mass index and energy expenditure.\u003csup\u003e22\u003c/sup\u003e OXT receptors are found in multiple organs as in uterus, breast, aorta, and esophagus\u003csup\u003e24\u003c/sup\u003e. OXT receptors over-expression in close linkage to adiposity and lipolysis in adipocytes was inherently noticeable.\u003csup\u003e24\u0026ndash;25\u003c/sup\u003e In MetS and prediabetes; it was pronouncedly reduced.\u003csup\u003e26\u0026ndash;28\u003c/sup\u003e Meanwhile salivary OXT positively associated with plasma, but not with urine OXT in women acutely ill with anorexia nervosa.\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRetinol-binding protein 4 (RBP-4)\u003c/strong\u003e is one of the most important adipokines that affects systemic insulin sensitivity and glucose homeostasis with link to insulin resistance and MetS in obesity\u003csup\u003e30\u003c/sup\u003e. It is a transport protein for vitamin A (retinol), synthesized mainly by hepatocytes followed by adipocytes\u003csup\u003e12\u003c/sup\u003e. RBP-4 may play a role in the pathogenesis of T2D by participates in the development of insulin resistance by impairing insulin signaling at both the receptor and post-receptor levels, as well as by stimulation of liver gluconeogenesis\u003csup\u003e31\u003c/sup\u003e. RBP-4 is potentially associated with an increased risk of developing cardiovascular disease, particularly among patients with obesity, mainly due to an increased expression of pro-inflammatory cell surface adhesion molecules and soluble pro-inflammatory factors and possibly due to an unfavorable lipid profile and an increased intima-media thickness\u003csup\u003e32\u003c/sup\u003e. Resistin is secreted as a 94-amino acid polypeptide with an inhibitory effect on adipocyte differentiation and an association with insulin resistance\u003csup\u003e12\u003c/sup\u003e. Human resistin is mainly secreted by peripheral blood mononuclear cells; it competes with lipopolysaccharide for the binding to Toll-like receptor 4 and is involved in the inflammation\u003csup\u003e12\u003c/sup\u003e. Activation of resistin in the pancreatic islet cells inhibits insulin signaling via suppression of cell surface glucose transporters and a pro-inflammatory mechanism that results in \u0026beta;-cell loss, thus, a pro-diabetic effect\u003csup\u003e5\u003c/sup\u003e. Oddly the gradual increase in resistin levels (ng/mL), though not ascribed any statistically marked variation, was appreciable in both normoglycemic and preDM MetS groups vs. controls\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSIRT 1 (sirtuin 1)\u003c/strong\u003e is a protein from the sirtuin family of nicotinamide adenine dinucleotide-dependent deacylases (SIRT1-7) that are thought to be responsible mainly for the cardiometabolic benefits of lean diets and exercise, delaying key aspects of aging, e.g., decline in vascular endothelial function, metabolic syndrome, ischemia-reperfusion injury, obesity, and cardiomyopathy. Sirtuin activity steadily decreases with increasing age, and the decline is further exacerbated by obesity and sedentary lifestyles\u003csup\u003e34\u003c/sup\u003e. It was recently found that the defect in endothelial sirtuin 1 deacetylase activity is associated with (a) elevated basal and stimulated levels of superoxide generation (via the FoxO1 over-acetylation mechanism) and (b) increased nuclear translocation of NF-kB (via p65 over-acetylation mechanism). Based on these findings, the novel function of sirtuin 1 was proposed, namely, the maintenance of endothelial glycocalyx, particularly manifest in conditions associated with sirtuin 1 depletion\u003csup\u003e35\u003c/sup\u003e. It was found of reduced levels in MetS patients\u003csup\u003e36\u003c/sup\u003e. \u003cstrong\u003eVisfatin\u003c/strong\u003e is a highly conserved, 52 kDa protein expressed in a variety of tissues and cell types, including adipocytes, being much more abundant in visceral fat than in subcutaneous fat\u003csup\u003e12\u003c/sup\u003e. Visfatin has the dual effects as an adipocytokine, namely, as a global insulin-imitator and local adipogenic\u003csup\u003e37\u003c/sup\u003e. It has nicotinamide phosphoribosyltransferase (NAMPT) activity and, hence, an insulin-mimicking/insulin-sensitizing effects\u003csup\u003e5\u003c/sup\u003e. It binds to insulin receptor at a different site from that of insulin and stimulates glucose uptake in adipocytes and muscle cells and suppresses glucose release from hepatocytes\u003csup\u003e12\u003c/sup\u003e. The \u003cstrong\u003eZBED3 (zinc finger, BED-type (ZBED) protein 3)\u003c/strong\u003e gene family comprises a closely related group of genes that contribute to the regulation of various functions by encoding regulatory proteins\u003csup\u003e38\u003c/sup\u003e. It was suggested that both Zbed3 and Axin2 have important roles in regulating Wnt activity while Wnt/b-catenin signaling has been shown to regulate adipogenesis and, thus, has a relationship with obesity and insulin resistance\u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e The study was approved by the Jordan University Hospital (JUH) Institutional Review Board (IRB). All procedures performed in the study were in accordance with the ethical standards of the IRB and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The potential study participants were approached randomly during their visits to the Family Medicine Clinic at the Jordan University Hospital (JUH). The participants were interviewed and their medical files were reviewed in order to assess the inclusion and exclusion criteria and in order to distribute them into the study groups. All potential candidates were informed thoroughly about the study; participants who agreed to take part in the study were asked to sign an informed consent form in Arabic. Data collection of patients\u0026rsquo; medical histories was conducted until May 2020. All study participants were coded according to the study arm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e This is a cross sectional study aimed to examine the relation between plasma levels of eighteen metabolic risk biomarkers (arranged alphabetically): Lipocalin, Nesfatin, Omentin,, Oxytocin, RBP-4 (retinol-binding protein-4), Resistin, SIRT 1 (sirtuin 1), Visfatin and ZBED3 (zinc finger, BED-type (ZBED) protein 3) in two groups of adult (18\u0026ndash;75 years) Jordanian patients, namely, 1) Metabolic syndrome (MetS) group that included 61 individuals who were overweight or obese (BMI\u0026thinsp;\u0026gt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e) with 3 or more of MetS criteria\u003csup\u003e40\u003c/sup\u003e. According to the new IDF definition, for a person to be defined as having the MetS, they must have central obesity (defined as waist circumference with ethnicity specific values)* plus any two of four additional factors. These four factors are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWithin the MetS group, any of the following individuals were included (\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eControl group that included 31 healthy individuals who were normoglycemic (a fasting plasma glucose (FPG)\u0026thinsp;\u0026lt;\u0026thinsp;100 mg/dL or a hemoglobin A1c (A1C)\u0026thinsp;\u0026lt;\u0026thinsp;5.7% 33)) and lean (BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePrediabetes - a FPG of 100\u0026ndash;125 mg/dL, or a 2-hour plasma glucose level of 140 mg/dL\u0026ndash;199 mg/dL during a 75-g oral glucose tolerance test (OGTT), or A1C of 5.7\u0026ndash;6.4%;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eType 2 diabetes mellitus - a FPG level of \u0026ge;\u0026thinsp;126 mg/dL, or OGTT, or random plasma glucose of 200 mg/dL or higher in a patient with classic symptoms of hyperglycemia or hyperglycemic crisis, or HbA1c level of 6.5% or higher\u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eThe following were exclusion criteria\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNon-fasting individuals\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePregnant or breast feeding/lactating women.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAny prior use of anti-diabetic agent such as (sulfonylureas, meglitinides, biguanides, thiazolidinediones, alpha-glucosidase inhibitors, or insulin) either for diabetes itself or for any other condition.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAny prior use of lipid lowering agents.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClinical evidence of autoimmune, life-threatening diseases, alcohol, drug abuse, and recently diagnosed and untreated endocrine disorder other than prediabetes or diabetes mellitus.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIndividuals with known inflammatory diseases such as the inflammatory bowel disease.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometric measurements\u003c/strong\u003e Weight and height were measured using a balance mounted stadiometer. Waist circumference (WC) was measured using a nonstretchable tape at the midpoint between the last rib and the upper iliac crest, and hip circumference (HC) was measured around the widest section of the buttocks. Body mass index (BMI) was calculated as body weight (kg) divided by the square of height (m\u003csup\u003e2\u003c/sup\u003e). WHR and WHtR were calculated by dividing the WC (cm) by HC (cm) and height, respectively. Systolic blood pressure (SBP) and diastolic blood (DBP) pressures were measured using an electronic sphygmomanometer. Adiposity and atherogenecity indices were calculated using formulae\u003csup\u003e42\u0026ndash;44\u003c/sup\u003e. Analysis of HbA1c, FPG and lipid profile were conducted,\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e This cross sectional study meant to compare plasma and saliva levels of cardiometabolic risk factors in Control group of 30 participants who were apparently healthy, lean (BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 Kg/m2), and normoglycemic (HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;5.7%, FBS\u0026thinsp;\u0026lt;\u0026thinsp;100 mg/dL), and two groups of overweight (BMI\u0026thinsp;\u0026gt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e) or obese (BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) drug-na\u0026iuml;ve MetS subjects, as defined by Alberti et al.\u003csup\u003e45\u003c/sup\u003e The cases group included 29 normoglycemic MetS and 29 pre-diabetes MetS (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Individuals with any of the following assessed candidates with these criteria were excluded from the study: \u003csup\u003e16, 33, 36,45\u003c/sup\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNon fasting individuals\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAny woman who is pregnant or breast feeding.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eClinical evidence of autoimmune or life threatening disease (alcohol/drug abuse/recently diagnosed and untreated an endocrine disorder.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIndividuals with known inflammatory diseases such as the bowel inflammatory disease.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObesity secondary to endocrine derangement other than DM.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAny prior treatment with any kind of antidiabetic medications used for diabetes or any other medical condition.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinical settings and duration\u003c/strong\u003e The study was conducted at the Family Medicine Clinic and the General Laboratories of the Jordan University Hospital (JUH) in accordance with the Declaration of Helsinki. The project was approved by the IRB (Institutional Review Board (IRB) Bioethics Committee of the Jordan University Hospital (JUH) and the Scientific Research Committee at the School of Pharmacy, the University of Jordan. The eligible participants were informed in detail about the study and gave their written consent in Arabic. Participation in the study was voluntary. Furthermore, they were interviewed about their medical and family history alongside with reviewing their medical file to collect clinical information and laboratory data. Patient recruitment started at the beginning of July 2017 and ended by the beginning of December 2017.The anthropometric data such as height, waist circumference, blood pressure, and BMI were measured using specific tools. A venous blood was drawn from each candidate after 12 hours fasting to assess the levels of fasting plasma glucose (FPG) and lipid profile. The biochemical analysis of fasting lipid profile (HDL-C, LDL-C, TG, and TC), FPG, and HbA1c were performed for each participant. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e displays the indices that were used in this study. Lipocalin 2, oxytocin, and nesfatin were procured from Abcam (Cambridge, MA, USA). Omentin, RBP-4 (retinol-binding protein-4), Resistin, SIRT 1 (sirtuin 1), Visfatin and ZBED3 (zinc finger, BED-type (ZBED) protein 3) were obtained from MyBiosourse, Inc. (San Diego, CA, USA). Markers\u0026rsquo; plasma and salivary levels were assayed according to manufacturers\u0026rsquo; instructions with intra- and interassay precisions of \u0026lt;\u0026thinsp;10-\u0026lt;12%. Harvested plasma (from lithium heparin collection tubes centrifuged at 4000 rpm for 10 minutes) were immediately stocked at -80oC until analysis. All saliva samples were collected via passive drool method into SalivaBio Saliva Collection Device (Salimetrics, Carlsbad, CA, USA). Immediately after collection, saliva samples were centrifuged for 15 min at 4000 rpm to remove any particles or sediments and supernatants using 2ml cryovials were stored at \u0026minus;\u0026thinsp;70 ◦C until analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e Data were entered and analyzed via IBM SPSS\u0026copy; statistics 22 (SPSS, Inc., USA). Shapiro-Wilk test for was used for the assessment of normality of data distribution. Categorical data were expressed as numbers (%), normally distributed continuous data were expressed as mean (SD), and not normally distributed continuous data were expressed as median [interquartile range]. Gender differences between the study groups were tested using Chi-square test. While comparing continuous independent variables between the study groups we used the independent sample t-test for normally distributed data and Mann-Whitney test for data that were not normally distributed. Spearman correlation test was used for the assessment of correlations between plasma and salivary metabolic risk biomarkers as well as of selected biomarkers and clinical and laboratory parameters in both the total study sample and the MetS patients alone. Correlations were considered very strong, if correlation coefficient was at least 0.8; moderately strong, if the coefficient was 0.6 up to 0.8; fair, if the coefficient was 0.3 to 0.5 and poor if the coefficient was less than 0.3\u003csup\u003e46\u003c/sup\u003e. For all statistical tests, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was determined as statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic and clinical characteristics (\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbout three quarters of the participants were females, with gender distribution similar between the two study groups (P value\u0026thinsp;=\u0026thinsp;0.585). Among MetS patients, almost half were normoglycemic, about 43% were prediabetic and about 8% were diabetic. The average age of study participants was 48.6 years, with MetS group being significantly older than the control group (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In accordance to the study selection criteria, glycemic (FPG and A1c) and lipid parameters (TG, HDL-C and non-HDL-C), adiposity indices (BMI, WHR, WtHR, C-index, BAI, LAP, VAI) and atherogenicity indices (AIP, TC/HDL-C, LDL-C/HDL-C, non-HDL-C/HDL-C and TG/HDL-C) were all significantly higher in the MetS group compared to the control group (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlasma and salivary levels of cardiometabolic risk biomarkers of pharmacotherapy in MetS patients (\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the plasma cardiometabolic risk biomarkers of pharmacotherapy, plasma (but not salivary) lipocalin levels were significantly higher (\u0026gt;\u0026thinsp;17-folds; P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the MetS group compared to the control group. Salivary nesfatin (unlike plasma nesfatin) levels were substantially higher in MetS participants vs. normoglycemic lean controls. Notably, plasma SIRT1 levels were pronouncedly greater (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in MetS recruits in comparison to control\u0026rsquo;s levels. Conversely and oddly; salivary SIRT1 concentrations in MetS pool markedly exceeded those of controls\u0026rsquo; salivary levels. Collectively salivary and blood levels of omentin, RBP, resistin, visfatin and ZBED3 lacked comparably significant variations in MetS cases vs. those of study controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExcept for OXT; Lack of Correlations between salivary and plasma levels of cardiometabolic risk biomarkers of pharmacotherapy (\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExceptionally oxytocin, amongst 9 cardiometabolic risk biomarkers of pharmacotherapy studied, had proportional significant correlations between plasma and saliva levels, in both total sample and MetS patients (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations of plasma and salivary oxytocin with clinical and biochemical parameters, adiposity and atherogenecity indices (\u003c/strong\u003eTables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLikewise, in the total sample plasma OXT (unlike salivary OXT) correlated significantly though inversely with both SBP and FBG. Collectively both blood and saliva OXT in the total study pool, as well as the remaining biomarkers; lacked comparably substantial associations with both adiposity and atherogenecity indices and clinical parameters of fasting lipid profile. Interestingly of MetS pool; markedly Proportional correlations of plasma (but not salivary) OXT with TG, and adiposity indices of LAP and VAI, and all atherogenecity indices were delineated.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003eLipocalin\u003c/b\u003e The MetS patients in our study had 17-folds increase in plasma lipocalin level compared to control; however, there was no significant difference in the salivary lipocalin levels between study groups. Circulating lipocalin-2 levels were previously shown to be higher in obese than in lean humans\u003csup\u003e47\u003c/sup\u003e and in MetS patients vs. control\u003csup\u003e11, 48\u003c/sup\u003e. Notably, no difference in salivary lipocalin-1 level was found between obese and normal weight individuals\u003csup\u003e49\u003c/sup\u003e. However, it is worth mentioning that, like irisin, lipocalin levels are associated with periodontitis and its severity\u003csup\u003e50\u003c/sup\u003e. We found lack of correlation between the plasma and salivary lipocalin level. None of the previous studies investigated such correlations in MetS patients.\u003c/p\u003e \u003cp\u003e \u003cb\u003eNesfatin\u003c/b\u003e We found no difference in plasma nesfatin levels between the two study groups, like the results of the previous study conducted by our group\u003csup\u003e16\u003c/sup\u003e. However, data from Saudi Arabia showed that patients with MetS had significantly lower nesfatin levels compared to the control\u003csup\u003e17\u003c/sup\u003e. On the contrary, we showed elevated salivary nesfatin level in MetS patients in comparison with normal individuals. None of the previous studies investigated salivary nesfatin in MetS patients and its correlation with the plasma level.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOmentin\u003c/b\u003e Neither plasma, nor salivary omentin were different between the MetS patients and the controls in our study. In our previous research we showed that in the Mets-pre/T2DM group, circulating levels of omentin-1were significantly lower vs. respective MetS-controls\u003csup\u003e51\u003c/sup\u003e. This is in contrast with the findings of decreased omentin-1 levels in pre-diabetic, T1DM, and newly diagnosed, untreated T2DM patients\u003csup\u003e12\u003c/sup\u003e as well as in morbidly obese women with MetS\u003csup\u003e19\u003c/sup\u003e. Decreased omentin-1 levels are generally associated with insulin resistance, diabetes, and metabolic syndrome, and atherosclerotic cardiovascular diseases. However, omentin-1 increases to counteract the acute phase after onset of these diseases\u003csup\u003e18\u003c/sup\u003e. Our study found no correlation between plasma and salivary omentin, in contrast to results of a very recent study that demonstrated correlation omentin in patients with periodontal disease and concomitant T2DM\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOxytocin\u003c/b\u003e Neither serum, nor salivary oxytocin significantly differed between the MetS patient and controls in our study. We previously showed that oxytocin levels were significantly lower in both MetS groups (prediabetic and T2DM) than in MetS-normoglycemic subjects\u003csup\u003e4; 26\u0026ndash;28\u003c/sup\u003e. However, this discrepancy may be explained by the differences in clinical and demographic characteristics between the studies (e.g., age of the control about 42 years in our study as opposed to about 29 years in\u003csup\u003e28\u003c/sup\u003e study). Qian et al.\u003csup\u003e52\u003c/sup\u003e also reported that serum oxytocin levels were decreased in obese adults as well as in adults with type 2 diabetes. Furthermore, Yuan et al.\u003csup\u003e53\u003c/sup\u003e demonstrated that patients with MetS had significantly lower oxytocin levels than did patients without MetS. In addition, a study in children demonstrated that oxytocin level is significantly lower in obese compared with non-obese patients and lower in obese patients with MetS compared to those without\u003csup\u003e23\u003c/sup\u003e. One of the most prominent results of our study is the positive correlation of oxytocin levels between plasma and saliva in both the total sample and the MetS patients. This agrees with the results by Hoffman et al.\u003csup\u003e29\u003c/sup\u003e in women with anorexia nervosa but contradicts the results of the studies in healthy volunteers of lack of correlation between plasma and salivary oxytocin\u003csup\u003e54\u0026ndash;55\u003c/sup\u003e. Interestingly, saliva contamination by blood was shown to affect oxytocin detection in saliva by transferrin presence\u003csup\u003e56\u003c/sup\u003e. However, to the best of our knowledge, none of the previous studies investigated salivary-circulating oxytocin correlations in patients with MetS. As demonstrated in our previous study in the entire MetS study population (normoglycemic, prediabetic and T2DM patients), plasma oxytocin correlated negatively with HbA1c, FPG, resistin, adiponectin and leptin\u003csup\u003e28\u003c/sup\u003e. In the current study, plasma, but not salivary oxytocin correlated mainly with lipid parameters ad atherogenicity indices (TGs, LAP, non-HDL-C/HDL-C, VAI, LDL-C/HDL-C, TC/HDL-C, TG/HDL-C and AIP) in the MetS patients. Therefore, despite expectations, salivary OXT testing might not be a useful tool for non-invasive detection and assessment of MetS.\u003c/p\u003e \u003cp\u003e\u003cb\u003eRBP4\u003c/b\u003e Our study found no difference in plasma or salivary RBP level between the MetS and the control, in agreement with our previous report that demonstrated lack of discrepancy in RBP4 circulating levels in both MetS groups (non-diabetic and preDM) vs. controls\u003csup\u003e33\u003c/sup\u003e. It was shown that the RBP-4 can be high or unchanged in glucose intolerance, type 2 diabetes, insulin resistance or metabolic syndrome\u003csup\u003e57\u003c/sup\u003e. RBP-4 concentrations were higher in patients with MetS than in controls\u003csup\u003e58\u003c/sup\u003e. In a large, population-based, cohort study the increased RBP4 serum levels were strongly associated with the presence and the number of components of MetS in a 65\u0026thinsp;+\u0026thinsp;Caucasian population\u003csup\u003e31\u003c/sup\u003e. Besides, circulating RBP4 levels and RBP4 mRNA expression in visceral and subcutaneous abdominal adipose tissue are increased in obese patients compared with lean subjects\u003csup\u003e12\u003c/sup\u003e. It has been suggested that RPB4 concentrations may not be related necessarily to obesity itself, but to the location of the adipose tissue and are more closely associated with visceral fat levels, hence, appear to constitute the best indicator of intra-abdominal adipose mass\u003csup\u003e32\u003c/sup\u003e. In the Third Generation Cohort of the Framingham Heart Study, higher plasma RBP4 concentrations were not only associated with cross-sectional presence of MetS but also prospectively associated with incident MetS\u003csup\u003e59\u003c/sup\u003e. We found no correlation between the plasma and salivary RBP levels, and, to the best of our knowledge, none of the previous studies have assessed the salivary RBP levels in MetS patients.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResistin\u003c/b\u003e Our study shows that both plasma and salivary resistin did not differ between the MetS patients and the control. In our previous research, resistin levels were significantly higher in both MetS groups (prediabetic and T2DM) than in MetS-only subjects\u003csup\u003e28\u003c/sup\u003e, however, in a further study by our group that had study limbs similar to our current study design, the gradual increase in resistin levels, though not ascribed any statistically marked variation, was appreciable in both normoglycemic and preDM/MetS groups vs. controls\u003csup\u003e33\u003c/sup\u003e. Other studies showed that the circulating levels of resistin are upregulated in T2DM\u003csup\u003e5\u003c/sup\u003e. Our results are supported by that of a meta-analysis that showed no difference in the concentrations of resistin in saliva between individuals with and without obesity\u003csup\u003e60\u003c/sup\u003e. However, in another study, resistin concentrations were significantly higher in T2DM saliva\u003csup\u003e5\u003c/sup\u003e. There was a significant correlation between the salivary and serum resistin levels in healthy volunteers\u003csup\u003e8\u003c/sup\u003e but not in our whole sample or MetS only patients. It is worth noting, that none of the previous researchers investigated such correlations in MetS patients.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSIRT 1\u003c/b\u003e We found decreased plasma levels but increased salivary levels of SIRT 1 in patients with MetS when compared with the controls. This agrees with the results of our previous study where MetS patients had lower SIRT 1 level vs. controls\u003csup\u003e36\u003c/sup\u003e. We found no reports on salivary SIRT 1 in relation to MetS. This salivary biomarker was shown to be increased in patients with periodontal disease\u003csup\u003e61\u003c/sup\u003e. No correlation was detected between the plasma and the salivary SIRT 1 levels in our study.\u003c/p\u003e \u003cp\u003e\u003cb\u003eVisfatin\u003c/b\u003e We found that neither plasma, nor salivary visfatin differed between the MetS and the control groups. This contradicts the results of the studies which showed increased plasma visfatin in patients with overweight/obesity, T2DM, MetS and CVD\u003csup\u003e62\u0026ndash;63\u003c/sup\u003e. In another study both the circulating and the salivary levels of visfatin were shown to be upregulated in T2DM\u003csup\u003e5\u003c/sup\u003e. The absence of correlation between the plasma and the salivary visfatin levels agrees with the study in healthy volunteers where no significant correlation between salivary and serum visfatin levels was observed\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eZBED3\u003c/b\u003e There was no difference in plasma and salivary ZBED3 levels between the MetS patients and the controls in our study. We previously also found lack of difference in plasma ZBED3 level between MetS (both preDM and normoglycemic) and the controls\u003csup\u003e64\u003c/sup\u003e. However, in two studies from China, circulating Zbed3 levels were significantly higher in individuals with impaired glucose tolerance and newly diagnosed T2DM relative to those with normal glucose tolerance\u003csup\u003e65\u003c/sup\u003e and, similarly, in newly diagnosed MetS patients than in non-MetS subjects\u003csup\u003e66\u003c/sup\u003e. There might be an inter-ethnic difference in ZBED3 involvement in MetS patients. No correlation was found between the plasma and the salivary ZBED levels in the whole study sample and in the MetS group. To the best of our knowledge, there is no previous research on ZBED3 in saliva.\u003c/p\u003e \u003cp\u003eAssessment of proteins from different functional classes is a plausible strategy to improve predictive ability, for example of T2DM. Notably, low-molecular-weight proteins (\u0026lt;\u0026thinsp;20 kDa) are more prevalent (14.5%) in the salivary proteome as compared to only 7% for the plasma proteome\u003csup\u003e67\u003c/sup\u003e. In a study\u003csup\u003e68\u003c/sup\u003e that involved individuals with T2DM, 65 salivary proteins demonstrated a greater than two-fold difference compared to control; a majority of the differentially abundant proteins belong to pathways regulating metabolism and immune response. Importantly, the study found a trend of relative increase in the salivary proteins abundance with progression from the pre-diabetic to the diabetic state. On the other hand, in a study of 27 different cytokines involving 50 healthy adults there was little correlation between the plasma and salivary samples; therefore, it was concluded that substituting saliva for blood needs a great caution, and that relationships differ by biomarker\u003csup\u003e69\u003c/sup\u003e. We support the opinion that the data of salivary biomarkers\u0026rsquo; levels should be interpreted with caution as the type of sample (stimulated vs. unstimulated; whole vs. glandular), timing of sampling, sensitivity to preprocessing as well as presence of oral diseases are some of the confounding parameters may affect the biomarkers salivary levels\u003csup\u003e5\u003c/sup\u003e. Such cofounders include periodontitis, uneven salivary dilution level, or other exogenous factors\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAmong 9 cardiometabolic biomarkers, we found correlations between the plasma and the saliva for oxytocin. Salivary testing of oxytocin may be the promising noninvasive method of early detection/prediction/prevention/prognosis parameter of MetS/prediabetes.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eAdiposity indices (BMI, WHR (waist/Hip ratio), WtHR (waist/Height ratio), Conicity-index, BAI (Body adiposity index), LAP (Lipid accumulation Product), VAI (Visceral adiposity Index)) and atherogenicity indices (AIP (atherogenecity index of plasma)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate and publish\u003c/em\u003e\u003c/strong\u003e were obtained and hence implemented\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/strong\u003e All authors contributed equally towards manuscript conceptualization, composition, statistical analysis and final draft approval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e This study was funded by Deanship of Scientific Research/University of Jordan\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of Interest\u003c/em\u003e\u003c/strong\u003e: None\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Jaramillo P, G\u0026oacute;mez-Arbel\u0026aacute;ez D, L\u0026oacute;pez-L\u0026oacute;pez J, et al The role of leptin/adiponectin ratio in metabolic syndrome and diabetes. Hormone Mol Biol Clin Invest., 2014; 18(1):37\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1515/hmbci-2013-0053\u003c/span\u003e\u003cspan address=\"https://doi:10.1515/hmbci-2013-0053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJialal I, Devaraj S Subcutaneous adipose tissue biology in metabolic syndrome. Hormone Mol Biol Clin Invest., 2018; 33(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1515/hmbci-2017-0074\u003c/span\u003e\u003cspan address=\"https://doi:10.1515/hmbci-2017-0074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhadge AA, Khaire AA Leptin as a predictive marker for metabolic syndrome. Cytokine 2019; 121: 154735. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/j.cyto.2019.154735\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/j.cyto.2019.154735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasabri V, Shawakri E, Akour A, et al Cross-sectional correlates of increased IL-18 but reduced fetuin-A and oxytocin with adiposity and blood indices in metabolic syndrome patients with and without prediabetes. Ther Adv Endocrinol Metab. 2018; 9(12):329\u0026ndash;338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1177/2042018818788802\u003c/span\u003e\u003cspan address=\"https://doi:10.1177/2042018818788802\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrinivasan M, Meadows ML, Maxwell L Assessment of Salivary Adipokines Resistin, Visfatin, and Ghrelin as Type 2 Diabetes Mellitus Biomarkers. \u003cem\u003eBiochem Res Int.\u003c/em\u003e, 2018; 2018: 7463796. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1155/2018/7463796\u003c/span\u003e\u003cspan address=\"https://doi:10.1155/2018/7463796\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoo JA, Yan W, Ramachandran P, Wong DT Comparative human salivary and plasma proteomes. J Dent Res., 2010; 89(10):1016\u0026ndash;1023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1177/0022034510380414\u003c/span\u003e\u003cspan address=\"https://doi:10.1177/0022034510380414\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrinivasan M, Blackburn C, Mohamed M, et al Literature-based discovery of salivary biomarkers for type 2 diabetes mellitus. Biomarker Insights, 2015; 10:39\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.4137/BMI.S22177\u003c/span\u003e\u003cspan address=\"https://doi:10.4137/BMI.S22177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMamali I, Roupas ND, Armeni AK, et al Measurement of salivary resistin, visfatin and adiponectin levels. Peptides 2012; 33:120\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1016/j.peptides.2011.11.007\u003c/span\u003e\u003cspan address=\"http://doi:10.1016/j.peptides.2011.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrum RS, Duarte PM, Canto GL, et al Biomarkers in biological fluids in adults with periodontitis and/or obesity: A meta-analysis. J Ind Soc of Periodont., 2020; 24(3):191\u0026ndash;215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.4103/jisp.jisp_512_19\u003c/span\u003e\u003cspan address=\"https://doi:10.4103/jisp.jisp_512_19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTierney C, Bazou D, L\u0026ecirc; G, Dowling P, O'Gorman P Saliva-omics in plasma cell disorders- Proof of concept and potential as a non-invasive tool for monitoring disease burden. J Proteomics. 2021; 231:104015. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/j.jprot.2020.104015\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/j.jprot.2020.104015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang Y, Lee JH, Wang Y, Sweeney G Emerging clinical and experimental evidence for the role of lipocalin-2 in metabolic syndrome. Clin Exp Pharmcol Physiol 2012; 39(2):194\u0026ndash;199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1111/j.1440-1681.2011\u003c/span\u003e\u003cspan address=\"http://doi:10.1111/j.1440-1681.2011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 05557.x.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Jia X, Qiao J, et al Adipokines in reproductive function: a link between obesity and polycystic ovary syndrome. J Mol Endocrinol 2013; 50(2): R21\u0026ndash;R37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1530/JME-12-0247\u003c/span\u003e\u003cspan address=\"http://doi:10.1530/JME-12-0247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrancisco V, Ruiz-Fern\u0026aacute;ndez C, Pino J, et al Adipokines: Linking metabolic syndrome, the immune system, and arthritic diseases. Biochem Pharmacol 2019; 165: 196\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1016/j.bcp.2019.03.030\u003c/span\u003e\u003cspan address=\"http://doi:10.1016/j.bcp.2019.03.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo JJ, Wen FJ, Qiu D, Wang F Nesfatin-1 in lipid metabolism and lipid-related diseases. Clin Chim Acta, 2021; 522: 23\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cca.2021.08.005\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2021.08.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTekin T, Cicek B, Konyaligil N Regulatory Peptide Nesfatin-1 and its Relationship with Metabolic Syndrome. Euras J Med 2019; 51(3): 280\u0026ndash;284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.5152/eurasianjmed.2019.18420\u003c/span\u003e\u003cspan address=\"http://doi:10.5152/eurasianjmed.2019.18420\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Qudah SA, Kasabri V, Saleh MI, et al Cross-sectional correlates of nesfatin and lipopolysaccharide binding protein in metabolic syndrome patients with and without prediabetes. Hormone Mol Bio Clin Invest 2018; 36(3):\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1515/hmbci-2018-0035\u003c/span\u003e\u003cspan address=\"10.1515/hmbci-2018-0035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. http://doi:10.1515/hmbci-2018-0035.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlotibi MN, Alnoury AM, Alhozali AM Serum nesfatin-1 and galanin concentrations in the adult with metabolic syndrome. Relationships to insulin resistance and obesity. Saudi Med J., 2019; 40(1):19\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.15537/smj.2019.1.22825\u003c/span\u003e\u003cspan address=\"http://doi:10.15537/smj.2019.1.22825\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe T, Watanabe-Kominato K, Takahashi Y, et al Adipose Tissue-Derived Omentin-1 Function and Regulation. Compr Physiol. 2017; 7(3):765\u0026ndash;781. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1002/cphy.c160043\u003c/span\u003e\u003cspan address=\"http://doi:10.1002/cphy.c160043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuguet T, Quintero Y, Riesco D, et al New adipokines vaspin and omentin. Circulating levels and gene expression in adipose tissue from morbidly obese women. BMC Med Gen 2011; 12:60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1186/1471-2350-12-60\u003c/span\u003e\u003cspan address=\"http://doi:10.1186/1471-2350-12-60\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDooxa Nongrum A, Guru SR, K J N, Aghanashini S. Analysing adipokine Omentin-1 in periodontal disease and type-2 diabetes mellitus: An interventional comparative study. J Oral Biol Craniofac Res. 2022; 12(2):273\u0026ndash;278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1016/j.jobcr.2022.03.010\u003c/span\u003e\u003cspan address=\"http://doi:10.1016/j.jobcr.2022.03.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErdenebayar O, Kato T, Kawakita T, et al Effects of peripheral oxytocin administration on body weight, food intake, adipocytes, and biochemical parameters in peri- and postmenopausal female rats. Endocr J. 2021; 68(1):7\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1507/endocrj.EJ19-0586\u003c/span\u003e\u003cspan address=\"http://doi:10.1507/endocrj.EJ19-0586\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamerino C The New Frontier in Oxytocin Physiology: The Oxytonic Contraction. Int J Mol Sci. 2020; 21(14):5144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.3390/ijms21145144\u003c/span\u003e\u003cspan address=\"http://doi:10.3390/ijms21145144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBinay \u0026Ccedil;, Paket\u0026ccedil;i C, G\u0026uuml;zel S, Samancı N Serum Irisin and Oxytocin Levels as Predictors of Metabolic Parameters in Obese Children. J Clin Res Pediatr Endocrinol. 2017; 9(2):124\u0026ndash;131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.4274/jcrpe.3963\u003c/span\u003e\u003cspan address=\"https://doi:10.4274/jcrpe.3963\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCormack SE, Blevins JE, Lawson EA Metabolic Effects of Oxytocin. Endocr Rev. 2020; 41(2):121\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1210/endrev/bnz012\u003c/span\u003e\u003cspan address=\"http://doi:10.1210/endrev/bnz012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIwasa T, Matsuzaki T, Mayila Y, et al. The effects of chronic oxytocin administration on body weight and food intake in DHT-induced PCOS model rats. Gynecol Endocrinol. 2020; 36(1):55\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1080/09513590\u003c/span\u003e\u003cspan address=\"http://doi:10.1080/09513590\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2019.1631276.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAL-Nouaaimi MM, Kasabri V, Akour A, et al Evaluation of the Correlation of Oxytocin Plasma Levels and Metabolic Syndrome Biomarkers (Leptin, Adiponectin and Resistin) in newly diagnosed Type 2 Diabetes Patients in Jordan: A Cross Sectional Study. Jordan J Pharm Sci 2016; 9(2):115\u0026ndash;128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.12816/0033310\u003c/span\u003e\u003cspan address=\"http://doi:10.12816/0033310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Rawashdeh A, Kasabri V, Bulatova N, et al The correlation between plasma levels of oxytocin and betatrophin in non-diabetic and diabetic metabolic syndrome patients: A cross sectional study from Jordan. Diabetes Metab Syndr 2017; 11(1): 59\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1016/j.dsx.2016.08.008\u003c/span\u003e\u003cspan address=\"http://doi:10.1016/j.dsx.2016.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkour A, Kasabri V, Bulatova N, et al. Association of Oxytocin with Glucose Intolerance and Inflammation Biomarkers in Metabolic Syndrome Patients with and without Prediabetes. Rev Diab Stud: RDS 2018; 14(4): 364\u0026ndash;371. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1900/RDS.2017.14.364\u003c/span\u003e\u003cspan address=\"http://doi:10.1900/RDS.2017.14.364\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffman ER, Brownley KA, Hamer RM, Bulik CM Plasma, salivary, and urinary oxytocin in anorexia nervosa: a pilot study. Eating Behaviors 2012; 13(3): 256\u0026ndash;259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1016/j.eatbeh.2012.02.004\u003c/span\u003e\u003cspan address=\"http://doi:10.1016/j.eatbeh.2012.02.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnsar H, Mirzaei K, Malek A, et al Possible resting metabolic rate modification by the circulating RBP4 in obese subjects. Diabetes Metab Syndr 2015; 9(1): 19\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1016/j.dsx.2014.09.012\u003c/span\u003e\u003cspan address=\"http://doi:10.1016/j.dsx.2014.09.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMajerczyk M, Kocełak P, Choręza P, et al Components of metabolic syndrome in relation to plasma levels of retinol binding protein 4 (RBP4) in a cohort of people aged 65 years and older. J Endocr Invest 2018; 41(10):1211\u0026ndash;1219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1007/s40618-018-0856-6\u003c/span\u003e\u003cspan address=\"http://doi:10.1007/s40618-018-0856-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRychter AM, Skrzypczak-Zielińska M, Zielińska A, et al Is the Retinol-Binding Protein 4 a Possible Risk Factor for Cardiovascular Diseases in Obesity?. Int J Mol Sci 2020; 21(15): 5229. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.3390/ijms21155229\u003c/span\u003e\u003cspan address=\"http://doi:10.3390/ijms21155229\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoran AA, Albsoul-Younes A, Kasabri V, et al Correlates of resistin and retinol-binding protein 4 in metabolic syndrome patients with and without prediabetes. Horm Mol Biol Clin Investig. 2019; 37(3):\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/j/hmbci.2019.37.issue-3/hmbci-2018-0051/hmbci-2018-0051.xml\u003c/span\u003e\u003cspan address=\"http:///j/hmbci.2019.37.issue-3/hmbci-2018-0051/hmbci-2018-0051.xml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKane AE, Sinclair DA Sirtuins and NAD + in the Development and Treatment of Metabolic and Cardiovascular Diseases. Circ Res. 2018; 123(7):868\u0026ndash;885. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1161/CIRCRESAHA.118.312498\u003c/span\u003e\u003cspan address=\"http://doi:10.1161/CIRCRESAHA.118.312498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipphardt M, Song JW, Goligorsky MS Sirtuin 1 and endothelial glycocalyx. Pflugers Arch. 2020; 472(8): 991\u0026ndash;1002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1007/s00424-020-02407-z\u003c/span\u003e\u003cspan address=\"http://doi:10.1007/s00424-020-02407-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRkhaya SA, Bulatova N, Kasabri V, et al Increased malondialdehyde vs. reduced sirtuin 1 in relation with adiposity, atherogenicity and hematological indices in metabolic syndrome patients with and without prediabetes. Diabetes Metab Syndr. 2018; 12(6):903\u0026ndash;909. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1016/j.dsx.2018.05.013\u003c/span\u003e\u003cspan address=\"http://doi:10.1016/j.dsx.2018.05.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFantuzzi G Adipose tissue, adipokines, and inflammation. J Allergy Clin Immunol. 2005; 115:911\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1016/j.jaci.2005.02.023\u003c/span\u003e\u003cspan address=\"http://doi:10.1016/j.jaci.2005.02.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Li M, Ding Y, et al Identification of zinc-finger BED domain-containing 3 (Zbed3) as a novel Axin-interacting protein that activates Wnt/beta-catenin signaling. J Biol Chem. 2009; 284(11):6683\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1074/jbc.M807753200\u003c/span\u003e\u003cspan address=\"https://doi:10.1074/jbc.M807753200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia, Y, Yuan, L, Hu, W, et al Zinc-finger BED domain-containing 3 (Zbed3) is a novel secreted protein associated with insulin resistance in humans. J Intern Med 2014; 275: 522\u0026ndash;533. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1111/joim.12170\u003c/span\u003e\u003cspan address=\"http://doi:10.1111/joim.12170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational diabetes Federation (IDF). Worldwide definition of the metabolic syndrome. The IDF consensus worldwide definition of the Metabolic Syndrome 2006:1e19. https://\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Diabetes Association; 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes\u0026mdash;2021. \u003cem\u003eDiabetes Care\u003c/em\u003e. 2021; 44 (S1): S15\u0026ndash;S33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.2337/dc21-S002\u003c/span\u003e\u003cspan address=\"https://doi:10.2337/dc21-S002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnouper A, Kasabri V, Bulatova N, Suyagh M, et al. Plasma Carnitine, Choline, γ-Butyrobetaine, and Trimethylamine N-oxide, but not Zonulin, are reduced in overweight/obese Patients with Pre/diabetes or Impaired Glycemia. Int J Diabetes Develop Countr.; 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13410-022-01088-x\u003c/span\u003e\u003cspan address=\"10.1007/s13410-022-01088-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergman RN, Stefanovski D, Buchanan TA, et al A better index of body adiposity. Obesity (Silver Spring, Md.) 2013; 19(5): 1083\u0026ndash;1089. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1038/oby.2011.38\u003c/span\u003e\u003cspan address=\"https://doi:10.1038/oby.2011.38\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobi\u0026aacute;šov\u0026aacute; M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FERHDL). Clin Biochem.; 2001; 34:583\u0026ndash;8 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/s0009-9120(01)00263-6\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/s0009-9120(01)00263-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e(a)Alberti KGMM, Eckel RH, Grundy SM et al Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. \u003cem\u003eCirculation\u003c/em\u003e, 2009; 120(16):1640-5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1161/CIRCULATIONAHA.109.192644\u003c/span\u003e\u003cspan address=\"https://doi:10.1161/CIRCULATIONAHA.109.192644\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; (b) Al-Batsh MMW, Albsoul-Younes A, Kasabri V, et al. Proportional correlates of cystatin-C with pentraxin-3, visceral adiposity index and atherogenicity index of plasma but not blood indices in metabolic syndrome patients with and without prediabetes. \u003cem\u003eHorm Mol Biol Clin Investig\u003c/em\u003e. 2018; 36(3). doi: 10.1515/hmbci-2018-0058.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan YH. Biostatistics 104: Correlational analysis. Singapore Med J. 2003; 44(12):614\u0026ndash;619. https://PMID: 1477 0254\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Lam KS, Kraegen EW et al Lipocalin-2 is an inflammatory marker closely associated with obesity, insulin resistance, and hyperglycemia in humans. Clin Chem 2007; 53: 34\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1373/clinchem.2006.075614\u003c/span\u003e\u003cspan address=\"http://doi:10.1373/clinchem.2006.075614\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi J, Ma X, Zhou M, et al Serum lipocalin-2 levels positively correlate with coronary artery disease and metabolic syndrome. Cardiovasc Diabetol. 2013; 12:176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1186/1475-2840-12-176\u003c/span\u003e\u003cspan address=\"http://doi:10.1186/1475-2840-12-176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVors C, Drai J, Gabert L, et al Salivary composition in obese vs. normal-weight subjects: towards a role in postprandial lipid metabolism?. Int J Obes 2015; 39(9):1425\u0026ndash;1428. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1038/ijo.2015.71\u003c/span\u003e\u003cspan address=\"http://doi:10.1038/ijo.2015.71\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan A, G\u0026uuml;rb\u0026uuml;z N, \u0026Ouml;zbalci Fİ, et al Increase in serum and salivary neutrophil gelatinase-associated lipocalin levels with increased periodontal inflammation. J Appl Oral Sci: Revista FOB 2020; 28:e20200276. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1590/1678-7757-2020-0276\u003c/span\u003e\u003cspan address=\"http://doi:10.1590/1678-7757-2020-0276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahwaji R, Kasabri V, Bulatova N et al. Evaluation of correlations of Plasma Levels of Oxytocin, Omentin-1 and Irisin in Diabetic and Non-Diabetic Metabolic Syndrome Patients: A Cross Sectional Study in Jordan. Jordan Med J., 2017; 51(3): 97\u0026ndash;810\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian W, Zhu T, Tang B, et al Decreased circulating levels of oxytocin in obesity and newly diagnosed type 2 diabetic patients. J Clin Endocrinol Metab 2014;99:4683\u0026ndash;4689.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1210/jc.2014-2206\u003c/span\u003e\u003cspan address=\"http://doi:10.1210/jc.2014-2206\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan G, Qian W, Pan R, et al Reduced circulating oxytocin and High-Molecular-Weight adiponectin are risk factors for metabolic syndrome. Endocr J 2016; 63:655\u0026ndash;662. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1507/endocrj.EJ16-0078\u003c/span\u003e\u003cspan address=\"http://doi:10.1507/endocrj.EJ16-0078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJavor A, Riedl R, Kindermann H, et al Correlation of plasma and salivary oxytocin in healthy young men - experimental evidence. \u003cem\u003eNeuro Endocrinol Lett\u003c/em\u003e. 2014; 35(6):470-3. https://PMID: 25433841\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartins D, Gabay AS, Mehta M, Paloyelis Y Salivary and plasmatic oxytocin are not reliable trait markers of the physiology of the oxytocin system in humans. Elife. 2020; 9:e62456. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.7554/eLife.62456\u003c/span\u003e\u003cspan address=\"http://doi:10.7554/eLife.62456\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBehr GA, Patel JP, Coote M, et al A statistical method to calculate blood contamination in the measurement of salivary hormones in healthy women. \u003cem\u003eClin Biochem\u003c/em\u003e. 2017; 50(7\u0026ndash;8):436\u0026ndash;439. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1016/j.clinbiochem\u003c/span\u003e\u003cspan address=\"http://doi:10.1016/j.clinbiochem\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2016.12.005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarakas SE, Banaszewska B, Spaczynski RZ, et al Free fatty acid binding protein-4 and retinol binding protein-4 in polycystic ovary syndrome: response to simvastatin and metformin therapies. Gyn Endocr 2013; 29(5), 483\u0026ndash;487. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.3109/09513590.2013.774360\u003c/span\u003e\u003cspan address=\"http://doi:10.3109/09513590.2013.774360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabak O, Simsek G, Erdenen F et al The relationship between circulating irisin, retinol binding protein-4, adiponectin and inflammatory mediators in patients with metabolic syndrome. Arch Endocrinol Metab, 2017; 61(6):515\u0026ndash;523.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1590/2359-3997000000289\u003c/span\u003e\u003cspan address=\"http://doi:10.1590/2359-3997000000289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZachariah JP, Quiroz R, Nelson KP, et al Prospective Relation of Circulating Adipokines to Incident Metabolic Syndrome: The Framingham Heart Study. J Am Heart Assoc. 2017; 6(7): e004974. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1161/JAHA. 116.004974\u003c/span\u003e\u003cspan address=\"http://doi:10.1161/JAHA. 116.004974\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuffles LF, Hermont AP, Abreu LG, et al Association between obesity and adipokines levels in saliva and gingival crevicular fluid: A systematic review and meta-analysis. J Evid-Based Med., 2019; 12(4):313\u0026ndash;324.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1111/jebm.12363\u003c/span\u003e\u003cspan address=\"http://doi:10.1111/jebm.12363\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKluknavsk\u0026aacute; J, Krajč\u0026iacute;kov\u0026aacute; K, Boler\u0026aacute;zska B Possible prognostic biomarkers of periodontitis in saliva. Eur Rev Med Pharmacol Sci. 2021; 25(8):3154\u0026ndash;3161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.26355/eurrev_202104_25724\u003c/span\u003e\u003cspan address=\"http://doi:10.26355/eurrev_202104_25724\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang Y-H, Chang D-M, Lin K-C, et al Visfatin in overweight/obesity, type 2 diabetes mellitus, insulin resistance, metabolic syndrome and cardiovascular diseases: a meta-analysis and systemic review. Diabetes/Metab Res Rev 2011; 27:515\u0026ndash;527. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1002/dmrr.1201\u003c/span\u003e\u003cspan address=\"http://doi:10.1002/dmrr.1201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBayani M, Pourali M, Keivan M Possible interaction between visfatin, periodontal infection, and other systemic diseases: A brief review of literature. Eur J Dent. 2017; 11(3):407\u0026ndash;410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.4103/ejd.ejd_284_16\u003c/span\u003e\u003cspan address=\"http://doi:10.4103/ejd.ejd_284_16\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbuZayed R, Bulatova N, Kasabri V, et al. Correlates of zinc finger BED domain-containing protein 3 and ghrelin in metabolic syndrome patients with and without prediabetes. Horm Mol Biol Clin Investig. 2019; 37(3):\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/j/hmbci.2019.37.issue-3/hmbci-2018-0052/hmbci-2018-0052.xml\u003c/span\u003e\u003cspan address=\"http:///j/hmbci.2019.37.issue-3/hmbci-2018-0052/hmbci-2018-0052.xml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia Y, Yuan L, Hu W et al Zinc-finger BED domain-containing 3 (Zbed3) is a novel secreted protein associated with insulin resistance in humans. J Intern Med. 2014; 275(5):522\u0026ndash;33.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi\u003c/span\u003e\u003cspan address=\"http://doi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/joim.12170\u003c/span\u003e\u003cspan address=\"10.1111/joim.12170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu W, Tian B, Li X, et al Circulating Zbed3 Levels in Subjects With and Without Metabolic Syndrome. Metab Syndr Relat Disord 2017; 15(5):207\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1089/met.2016.0122\u003c/span\u003e\u003cspan address=\"http://doi:10.1089/met.2016.0122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoo JA, Yan W, Ramachandran P, Wong DT Comparative human salivary and plasma proteomes. J Dent Res 2010; 89(10):1016\u0026ndash;1023.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1177/0022034510380414\u003c/span\u003e\u003cspan address=\"http://doi:10.1177/0022034510380414\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao PV, Reddy AP, Lu X et al Proteomic identification of salivary biomarkers of type-2 diabetes. J Proteom Res., 2009; 8(1):239\u0026ndash;245. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1021/pr8003776\u003c/span\u003e\u003cspan address=\"http://doi:10.1021/pr8003776\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliamson S, Munro C, Pickler R, et al Comparison of biomarkers in blood and saliva in healthy adults. \u003cem\u003eNurs Res Pract.\u003c/em\u003e, 2012; 2012:246178. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1155/2012/246178\u003c/span\u003e\u003cspan address=\"http://doi:10.1155/2012/246178\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. IDF metabolic syndrome (MetS) world-wide definition\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003eRaised triglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"80.8080808080808%\"\u003e\n \u003cp\u003e\u0026ge; 1.7 mmol/l (150 mg/dL)or specific treatment for this lipid abnormality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003eReduced HDL- cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"80.8080808080808%\"\u003e\n \u003cp\u003e\u0026lt; 1.03 mmol/l (40 mg/dL) in males\u003c/p\u003e\n \u003cp\u003e\u0026lt; 1.29 mmol/l (50 mg/dL) in females\u003c/p\u003e\n \u003cp\u003eor specific treatment for this lipid abnormality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003eRaised blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"80.8080808080808%\"\u003e\n \u003cp\u003eSystolic: \u0026ge; 130 mmHg Or Diastolic: \u0026ge; 85 mmHg or treatment of previously diagnosed hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003eRaised fasting plasma glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"80.8080808080808%\"\u003e\n \u003cp\u003eFasting plasma glucose \u0026ge; 5.6 mmol/l (100 mg/dL)\u003c/p\u003e\n \u003cp\u003eor previously diagnosed Type 2 diabetes\u003c/p\u003e\n \u003cp\u003eIf \u0026gt; 5.6 mmol/l or 100 mg/dL, oral glucose tolerance test is strongly recommended but is not necessary to define presence of the syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*For Eastern Mediterranian and Middle East polulation, the measure of central obesity include waist circumference of \u0026ge; 94 cm for males and \u0026ge; 80\u0026nbsp;cm in females. N.B.: If body mass index is \u0026gt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e then central obesity can be assumed, and waist circumference does not need to be measured.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Comparison of demographic, clinical laboratory parameters, adiposity and atherogenicity indices between the study groups\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Characteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample (N=92), N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls (N=31), N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetS (N=61), N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDemographic and clinical characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e68 (73.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e24 (77.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e44 (72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.585\u003cstrong\u003e#\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.2112676056338%\"\u003e\n \u003cp\u003e24(26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.394366197183096%\"\u003e\n \u003cp\u003e7 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.394366197183096%\"\u003e\n \u003cp\u003e17 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes status among MetS patients\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePrediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e26 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.1020408163265305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.93877551020408%\"\u003e\n \u003cp\u003e5 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"47.72727272727273%\"\u003e\n \u003cp\u003eNormoglycemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"52.27272727272727%\"\u003e\n \u003cp\u003e30 (49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample (N=92), Mean (SD) or Median [interquartile range]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl (N=31), Mean (SD) or Median [interquartile range]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetS (N=61), Mean (SD) or Median [interquartile range]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e48.62 (11.21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e43.29 (11.72)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e51.11 (10.16)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP (mm Hg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e131.98 (15.21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e116.62 (10.22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e139.16 (11.35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBP (mm Hg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e81.88 (11.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e72.38 (10.18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e86.31 (9.22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFPG (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e91.90 [19.00]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e86.46 (7.44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.70 [18.00]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA1c (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.40 [0.8]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.10 [0.50]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.60 [0.70]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG (mg/dL)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e152.00 [141]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.16 (30.54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e171.00 [125.00]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e138.41 (39.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e128.05 (28.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e143.25 (39.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-C (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e49.51 (15.93)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e57.21 (112.37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e45.92 (16.24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-HDL-C (mg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e159.03 (44.17)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e147.00 [55.00]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e169.49 (41.03)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eTC (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e208.54 (45.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e193.23 (40.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e215.41 (46.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAdiposity indices\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e29.32 [9.99]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e23.53 (1.67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.37 [10.84]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.91 (0.06)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.88 (0.07)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.92 (0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHtR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.62 (0.09)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.54 (0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.66 (0.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.33 [0.13]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.30 (0.11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.33 [0.09]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.37 [12.48]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.10 (4.76)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.30 (8.70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e61.28 [75.28]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e24.29 [23.84]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.67 [81.47]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.35 [2.38]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.36 (0.56)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.15 [3.05]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAtherogenicity indices\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.49 [0.51]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.22(0.22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.59 [0.39]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC/HDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.31 [1.82]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.08 [1.76]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.71 [1.90]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C/HDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.01 [1.42]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.48 (1.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.14 [1.47]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-HDL-C/HDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.85 (4.44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.57 (1.18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.71 [1.91]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG/HDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.11 [3.87]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.86 (0.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.91 [3.84]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.154639175257732%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: AIP \u0026ndash; atherogenic index of plasma; A1c \u0026ndash; glycated hemoglobin; BAI - body adiposity index; BMI \u0026ndash; body mass index; C-index - conicity index; DBP \u0026ndash; diastolic blood pressure; FPG \u0026ndash; fasting plasma glucose; HDL- C \u0026ndash; high-density lipoprotein-cholesterol; LAP \u0026ndash; lipid accumulation product; LDL-C \u0026ndash; low-density lipoprotein-cholesterol; MetS \u0026ndash; metabolic syndrome; non-HDL-C \u0026ndash; non\u0026mdash;high-density lipoprotein-cholesterol; SBP \u0026ndash;systolic blood pressure; TC \u0026ndash; total cholesterol; TG \u0026ndash; triglycerides; WHR - waist to hip ratio ; WHtR - waist-to-height ratio;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eNormally distributed data are presented as means (SD), not normally distributed data are presented as median [interquartile range].\u003c/p\u003e\n\u003cp\u003e#Comparison between MetS and control was made by Chi square.\u003c/p\u003e\n\u003cp\u003e^ Comparison between MetS and Control was made by Mann-Whitney test.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Comparison of plasma and salivary cardiometabolic risk biomarkers of pharmacotherapy between the study groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample (N=92), Mean (SD) or Median [interquartile range]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl (N=31), Mean (SD) or Median [interquartile range]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetS (N=61), Mean (SD) or Median [interquartile range]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep.Lipocalin (ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e377700.00 [187750.00]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003e23375.05 (19902.87)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e397400.00 [195200.00]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003es.Lipocalin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e23480.00 [32565.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e21980.00 [31050.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e26780.00 [38680]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.479^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003ep.Nesfatin (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e506.00 [4286.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e546.00 [2254.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e482.00 [219.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.144^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003es.Nesfatin (pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e136.00 [21.00]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003e128.95 (12.82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e142.00 [25]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003ep.Omentin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e22.73 [22.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e18.73 [16.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e24.18 [32.91]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.157^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003es.Omentin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e284.92 [129.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e305.83 [134.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e281.67 [139.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.370^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep.OXT (pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1391.91 [849.00]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1565.36 [671.12]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1284.76 [923.52]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.081^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003es.OXT (pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e101.00 [92.4]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.11 [128.70]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e104.63 [202.47]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.595^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003ep.RBP4 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e28266.67 [3800.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e29082.53 (2287.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e27800.00 [3466.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.122^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003es.RBP4 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e237.50 [128.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e251.00 [121]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e213.60 (88.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.989^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003ep.Resistin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e24.01 [19.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e23.33 [19.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e25.10 [19.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.229^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003es.Resistin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e29.57 [32.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e32.32 (18.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e28.51 [32.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.667^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep.SIRT 1 (ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.78 [2.10]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.88 (0.79)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.40 [0.80]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003es.SIRT 1 (ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.81 [18.07]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.15 (9.29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.50 (12.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003ep.Visfatin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e29.31 [50.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e34.00 [57.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e28.13 [53.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.680^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003es.Visfatin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e433.38 [141.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e402.50 [133.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e448.25 [156.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.746^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003ep.ZBED3 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e0.25 [0.29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e0.19 [0.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e0.28 [0.34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.059^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003es.ZBED3 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"29.166666666666668%\"\u003e\n \u003cp\u003e0.26 [0.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.083333333333332%\"\u003e\n \u003cp\u003e0.28 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"26.041666666666668%\"\u003e\n \u003cp\u003e0.26 [0.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.208333333333333%\"\u003e\n \u003cp\u003e0.401^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: OXT \u0026ndash; oxytocin; p - plasma; RBP \u0026ndash; retinol binding protein; s - salivary; SIRT 1 - \u0026nbsp; sirtuin 1; ZBED - zinc finger, BED-type.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e Normally distributed data are presented as means (SD), not normally distributed data are presented as median [interquartile range].\u003c/p\u003e\n\u003cp\u003e#Comparison between MetS and control was made by Chi square.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e^ Comparison between MetS and Control was made by Mann-Whitney test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Except for OXT plasma and salivary concentrations; Lack of Correlations between plasma and salivary concentrations of cardiometabolic risk biomarkers of pharmacotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpearman\u0026apos;s correlation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetS patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eLipocalin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eNesfatin (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eOmentin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOXT (pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.259\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.307*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig. (2-tailed)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e\u003cstrong\u003e81\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e\u003cstrong\u003e52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eRBP4 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eResistin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eSIRT1 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eVisfatin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eZBED3 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.77777777777778%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: OXT \u0026ndash; oxytocin; RBP \u0026ndash; retinol binding protein; SIRT 1 - \u0026nbsp;sirtuin 1; VAI \u0026ndash; visceral adiposity index; ZBED - zinc finger, BED-type.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5a. Correlations of plasma and salivary oxytocin with clinical and demographic parameters and adiposity indices in the total sample\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.216494845360825%\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003eBAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003eFBG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003eA1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.185567010309279%\"\u003e\n \u003cp\u003eTG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003ep.OXT (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e-0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.250\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.280\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.238095238095237%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.238095238095237%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e\u003cstrong\u003e85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e\u003cstrong\u003e85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003es.OXT (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.238095238095237%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.238095238095237%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5b. Lack of Correlations of plasma and salivary oxytocin with atherogenecity indices in the total sample (continued)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLDL-Cl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHDL-C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNon-HDL-C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNon HDL-C/HDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eVAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTC/HDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLDL-C/HDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTG/HDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ep.OXT (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003es.OXT (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: AIP \u0026ndash; atherogenic index of plasma; A1c \u0026ndash; glycated hemoglobin; BAI - body adiposity index; BMI \u0026ndash; body mass index; C-index - conicity index; DBP \u0026ndash; diastolic blood pressure; FPG \u0026ndash; fasting plasma glucose; HDL- C \u0026ndash; high-density lipoprotein-cholesterol; LAP \u0026ndash; lipid accumulation product; LDL-C \u0026ndash; low-density lipoprotein-cholesterol; MetS \u0026ndash; metabolic syndrome; non-HDL-C \u0026ndash; non\u0026mdash;high-density lipoprotein-cholesterol; OXT \u0026ndash; oxytocin; p - plasma; s - salivary; SBP \u0026ndash; systolic blood pressure; TC \u0026ndash; total cholesterol; TG \u0026ndash; triglycerides; VAI \u0026ndash; visceral adiposity index; WHR - \u0026nbsp;waist to hip ratio -; WHtR - waist-to-height ratio\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e\u003cstrong\u003ea. Correlations of plasma and salivary oxytocin with clinical and demographic parameters and adiposity indices in MetS patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"35.483870967741936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.376344086021505%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.526881720430108%\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.376344086021505%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.376344086021505%\"\u003e\n \u003cp\u003eBAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.376344086021505%\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.451612903225806%\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.376344086021505%\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.376344086021505%\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.451612903225806%\"\u003e\n \u003cp\u003eFBG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.451612903225806%\"\u003e\n \u003cp\u003eA1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.376344086021505%\"\u003e\n \u003cp\u003eTG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003ep.OXT (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.35483870967742%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e-0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.317\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.974358974358974%\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.974358974358974%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"16.129032258064516%\"\u003e\n \u003cp\u003es.OXT (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.35483870967742%\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e-0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003e-0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.376344086021505%\"\u003e\n \u003cp\u003e-0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.974358974358974%\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.974358974358974%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.410256410256411%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6b. Correlations of plasma and salivary oxytocin with atherogenecity indices in MetS patients (continued)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLDL-C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHDL-C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNon HDL-C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon HDL-C/HDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC/HDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C/HDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG/HDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ep.OXT (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.305\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.318\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.351\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.319\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.277\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.307\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.307\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003es.OXT (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: AIP \u0026ndash; atherogenic index of plasma; A1c \u0026ndash; glycated hemoglobin; BAI - body adiposity index; BMI \u0026ndash; body mass index; C-index - conicity index; DBP \u0026ndash; diastolic blood pressure; FPG \u0026ndash; fasting plasma glucose; HDL- C \u0026ndash; high-density lipoprotein-cholesterol; LAP \u0026ndash; lipid accumulation product; LDL-C \u0026ndash; low-density lipoprotein-cholesterol; MetS \u0026ndash; metabolic syndrome; non-HDL-C \u0026ndash; non\u0026mdash;high-density lipoprotein-cholesterol; OXT \u0026ndash; oxytocin; p - plasma; s - salivary; SBP \u0026ndash; systolic blood pressure; TC \u0026ndash; total cholesterol; TG \u0026ndash; triglycerides; VAI \u0026ndash; visceral adiposity index; WHR - \u0026nbsp;waist to hip ratio -; WHtR - waist-to-height ratio\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lipocalin, Nesfatin, Omentin, Oxytocin, RBP-4 (retinol-binding protein-4), Resistin, SIRT 1 (sirtuin 1), Visfatin and ZBED3 (zinc finger, BED-type (ZBED) protein 3, adiposity, and atherogenicity indices, cardiometabolic risk, metabolic syndrome, prediabetes","lastPublishedDoi":"10.21203/rs.3.rs-2587738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2587738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAims and methods\u003c/strong\u003e This study aimed to compare and correlate pharmacotherapy biomarkers’ plasma and salivary levels (appraised using colorimetric assays of Lipocalin, Nesfatin, Omentin, Oxytocin, RBP-4 (retinol-binding protein-4), Resistin, SIRT 1 (sirtuin 1), Visfatin and ZBED3 (zinc finger, BED-type (ZBED) protein 3), adiposity, and atherogenicity indices in 61 normoglycemic and newly diagnosed drug naive pre-diabetic (PreDM) MetS (metabolic syndrome) patients vs. 29 lean, and normoglycemic controls. Intergroup Comparisons was conducted by ANOVA. Spearman rank correlation was also examined.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e About three quarters of the participants were females, with gender distribution similar between the two study groups (P = 0.585). Among MetS patients, almost half were normoglycemic, about 43% were prediabetic and about 8% were diabetic. The average age of study participants was 48.6 years, with MetS group being significantly older than the control group (P \u0026lt; 0.001). In accordance to the study selection criteria, glycemic (FPG and A1c) and lipid parameters (TG, HDL-C and non-HDL-C), adiposity indices (BMI, WHR, WtHR, C-index, BAI, LAP, VAI) and atherogenicity indices (AIP, TC/HDL-C, LDL-C/HDL-C, non-HDL-C/HDL-C and TG/HDL-C) were all significantly higher in the MetS group compared to the control group (P \u0026lt; 0.05). Among the plasma cardiometabolic risk biomarkers of pharmacotherapy, plasma (but not salivary) lipocalin levels and Salivary nesfatin (unlike plasma nesfatin) were significantly higher P \u0026lt; 0.05) in the MetS group compared to the normoglycemic lean controls. Notably, plasma SIRT1 levels were pronouncedly greater (P \u0026lt; 0.05) in MetS recruits in comparison to control’s levels. Conversely; salivary SIRT1 concentrations in MetS pool markedly exceeded those of controls’ salivary levels. Oddly and collectively salivary and blood levels of omentin, oxytocin, RBP-4, resistin, visfatin and ZBED3 lacked comparably pronounced discrepancies in MetS cases vs. those of study controls. Exceptionally oxytocin, amongst 9 cardiometabolic risk biomarkers of pharmacotherapy studied, had proportional significant correlations between plasma and saliva levels, in both total sample and MetS patients (P \u0026lt; 0.05). Plasma OXT in the total sample correlated significantly though inversely with both SBP and FBG (unlike salivary OXT). Interestingly of MetS pool; markedly Proportional correlations of plasma (but not salivary) OXT with TG, and adiposity indices of LAP and VAI, and all atherogenecity indices were delineated. Collectively both blood and saliva OXT in the total study pool, as well as the remaining biomarkers; lacked comparably substantial associations with both adiposity and atherogenecity indices and clinical parameters of fasting lipid profile.\u003c/p\u003e","manuscriptTitle":"The Clinical Utility of salivary oxytocin as a putatively surrogate early Risk Identification biomarker of nascent Metabolic Syndrome with and without prediabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-13 22:48:38","doi":"10.21203/rs.3.rs-2587738/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f8b11bd-39da-4515-bc87-1658e835ab47","owner":[],"postedDate":"March 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":19702796,"name":"Biological sciences/Biochemistry"},{"id":19702797,"name":"Biological sciences/Molecular biology"},{"id":19702798,"name":"Biological sciences/Physiology"},{"id":19702799,"name":"Health sciences/Biomarkers"},{"id":19702800,"name":"Health sciences/Endocrinology"},{"id":19702801,"name":"Health sciences/Medical research"},{"id":19702802,"name":"Health sciences/Molecular medicine"}],"tags":[],"updatedAt":"2023-08-18T09:44:22+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-13 22:48:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2587738","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2587738","identity":"rs-2587738","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}