Association of Visfatin gene polymorphism with obesity related metabolic disorders among Pakistani population; a case control study

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The influence of gene variants involved in hormone synthesis or action could explain the conflicting results when considering the relationship between visfatin plasmatic levels and obesity. However, there is a lack of research on these polymorphisms. This study found that the SNPs for visfatin rs2302559 and rs1215113036 were statistically significantly associated with the metabolic factors at p value 0.001 and odd ratio suggested that there is a great chance of developing the disorder if these variations are present up to at 129.40. Frequency of the mutant alles were to be found in higher in frequency. Biological sciences/Biotechnology Biological sciences/Biological techniques/Bioinformatics Biological sciences/Biological techniques/Electrophysiology Biological sciences/Biological techniques/Genomic analysis Biological sciences/Biological techniques/Sequencing Diabetes Hypertension vastatin genetic variation Figures Figure 1 Figure 2 Figure 3 Introduction Obesity is a global epidemic that is linked to a number of chronic diseases, including cancer, diabetes, heart disease, and hypertension, despite the fact that it can be avoided(Chooi, Ding, & Magkos, 2019 ; Wagner & Brath, 2012 ). Gall bladder disease, insulin resistance, high blood pressure, sleep apnea, inflammation, dyspnea, non-alcoholic fatty liver disease, gestational diabetes mellitus, cancer, type 2 diabetes mellitus, and metabolic syndrome are among the comorbidities linked to obesity(Kushner & Kahan, 2018 ). Diabetes, dyslipidaemia, high blood pressure, and abdominal obesity are all threats of metabolic syndromes. A high-calorie, high-cholesterol diet paired with a sedentary lifestyle, smoking, drinking alcohol, and gaining weight as an adult are all risk factors for metabolic syndrome, which is linked to obesity. Surprisingly, variations in lifestyle can affect the susceptibility to metabolic syndrome due to genetic variations in insulin resistance and fat distribution(Han & Lean, 2016 ; Mendrick et al., 2018 ). The adipose tissue's release of different adipokines may be one of the main mechanisms underlying these lifestyle-related illnesses(Wnuk et al., 2020 ). Visfatin, an adipokine released by adipose tissues, and it has been linked to obesity and inflammation in the blood. Visfatin performs various biological functions in the human body including immunomodulation, catalyzing distinct cellular functions and anti-apoptosis(Haddad, Nori, & Hamza, 2018 ). Visfatin may be connected to the pathophysiology of diabetes and is essential for the establishment of the metabolic syndrome, according to previous study(Wnuk et al., 2020 ). Some SNPs for Visfatin that were discovered have been linked to problems associated with obesity and glucose/lipid metabolism. Additionally, improvements in insulin sensitivity and glucose tolerance have both been linked to increases in visfatin concentration in obese patients who have been trained in aerobic exercise. Therefore, Visfatin may be a candidate gene for changes in glucose and obesity-related phenotypes brought on by aerobic exercise training, and its gene polymorphisms may be the reason why different reactions to the same activities are observed in different people(Kaan et al.; Ooi, Ong, Heng, Loke, & Lee, 2016 ; Younes, Ibrahim, Al-Jurf, & Zayed, 2021 ). The aim of the current study is to investigate the association of visfatin SNPs including rs2302559 and rs1215113036 with the obesity-related metabolic syndrome. Material And Methods This study included 300 subjects of either sex, between the ages 30 to 60 years divided into two equal groups. Control group (n = 150) had normal healthy individuals with BMI 18.5–24.9 kg/m 2 , in Metabolic disorders group (n = 150) included obese individual which had BMI ≥ 25 kg/m 2 with T2DM and other component of metabolic disorder. Subjects suffering from Chronic medical conditions like endocrinological problems, Cardiovascular and renal diseases were excluded from the study. A Written informed consent along with clinical history was sought from each subject. All cases were selected by non-probability purposive sampling technique. 6 ml fasting blood was collected from all subjects for Biochemical analysis and genetic analysis. Serum Visfatin by ELISA techniques, fasting blood glucose and lipid profile by kit method Genomic DNA from whole blood were isolated using Kit Method. Visfatin gene primers were designed using online software Primer-1 ®. Specificity of all designed primers were confirmed using online BLAST® program/software. Genotyping of Visfatin gene (rs2302559 and rs1215113036) were be performed by using T-ARMs PCR analysis. The fragments obtained were analyzed using agarose gel electrophoresis stained with ethidium bromide. The confirmation of sequence was done using direct DNA Sequencing and sequencing file were analysed at Mega11 software. Approval of the study was given by IRB of Dow University of Health Sciences (DUHS). (IRB-1969/DUHS/Approval /2021/353. The complete study carried out in compliance with the relevant guideline and regulation of the mentioned authority. All statistical analyses were performed using SPSS software (SPSS Inc. version 20, Chicago, IL, USA). We evaluated the association of demographic variables between case and controls by Chi Square test and mean differences of continuous variables between case and controls by independent sample t-test. The relative associations between genotypes and cases and controls were assessed using to calculate odds ratios and 95% of confidence intervals. A two-tailed P < 0.05 was considered statistically significant. Results Demographic Characteristics of MetS Patients and Controls Demographic information was gathered and collated from patients with the metabolic syndrome caused by obesity. MetS was significantly associated with age group in the context of gender and age groups. Among contrast, in individuals with HTN, obesity, and diabetes, MetS was found to be significantly (p-value < 0.01) linked with medical history. In individuals with metabolic syndrome in their mother's family history, siblings, or at least one instance recorded in the family history, significant outcomes were found, as shown in Table 1 . Table 1 Demographic Characteristics of MetS Patients and Controls Groups Controls Cases Chi-Square test N % N % Value P-value Age Years (Mean ± SD) 37.90 8.946 47.53 9.183 -9.196 < 0.001* Gender Male 76 57.1 57 42.9 4.876 0.027 Female 74 44.3 93 55.7 Medical History HTN No 147 66.5% 74 33.5% 91.569 < 0.001 Yes 3 3.8% 76 96.2% Diabetes No 150 100.0% 0 .0% 300.00 < 0.001 Yes 0 0% 150 100.0% Obesity No 150 65.2% 80 34.8% 91.304 < 0.001 Yes 0 0% 70 100.0% Dyslipidemia Hyperlipidemia No 150 50.0% 150 50.0% Yes - - - - Family History No 113.75 49.6% 83.5 50.4% 19.74 < 0.001 Yes 36.25 51.4% 66.5 48.6% MetS Association with General Physical Features When compared to persons with general physical characteristics, patients with metabolic syndrome had significantly (p-value < 0.01) higher levels of weight, body mass index, systolic and diastolic blood pressure, and pulse rate as shown in Table 2 . Table 2 MetS Association with General Physical Features Groups Controls Cases t-test Statistics Mean SD Mean SD t P-value Weight (kg) 63.73 6.879 74.66 16.172 -7.615 < 0.001 Height (m) 1.6702 .08577 1.6631 .07904 .745 .457 BMI (Kg/m2) 22.8517 1.89748 27.0099 5.62306 -8.582 < 0.001 Circumferences (in) 12.2807 1.12731 12.1127 .83145 1.469 .143 Systolic Blood Pressure 119.98 8.180 132.91 17.188 -8.317 < 0.001 Diastolic Blood Pressure 76.40 6.801 79.99 10.305 -3.558 < 0.001 Pulse 78.93 7.202 81.80 5.531 -3.866 < 0.001 MetS Patient’s Biochemical Analysis Additionally, both samples—cases and controls—were subjected to a biochemical study. In patients with MetS compared to healthy people, biochemicals such serum visfatin, serum cholesterol, triglycerides, LDL and VLDL cholesterol, and fasting blood sugar were significantly (p-value < 0.01) higher as shown in Table 3 . Table 3 Biochemicals Analysis of Patients with MetS Groups Controls Cases t-test Statistics Mean SD Mean SD t P-value Serum Visfatin 2.8489 1.23514 10.0777 5.13731 -16.756 < 0.001 Fasting Blood Sugar 92.9667 15.59617 231.1733 83.91437 -19.832 < 0.001 Serum Cholesterol 174.27 16.281 184.97 28.539 -3.989 < 0.001 Triglycerides 119.35 16.335 126.33 23.967 -2.950 < 0.001 LDL C 92.25 12.487 99.03 19.613 -3.568 < 0.001 VLDL C 23.98 4.281 25.49 4.128 -3.103 0.002 HDL C 48.03 6.110 47.79 6.892 .328 0.743 MetS Correlation with General physical and Biochemical Factors In contrast to patients, results from Pearson correlation analysis indicated a significant (p-value < 0.01) negative correlation between MetS and diastolic blood pressure in healthy individuals. Overall findings revealed a significant (p-value < 0.01) positive correlation between visfatin and several variables, including age, weight, BMI, systolic blood pressure, pulse rate, fasting blood sugar, serum cholesterol, triglycerides, and LDL cholesterol as shown in Table 4 . Table 4 MetS Correlation with Various Factors Overall Group control Cases Correlation correlation coefficient P-value correlation coefficient P-value correlation coefficient P-value Serum Visfatin (ng/mL) 1 1 1 Age .275** < 0.001 − .073 .376 − .103 .211 Weight .294** < 0.001 − .050 .545 .027 .742 Height − .007 .900 − .002 .984 .049 .555 BMI (Kg/m2) .306** < 0.001 − .068 .407 .000 .992 Circumferences − .037 .524 − .087 .290 .082 .319 SBP .253** < 0.001 − .031 .703 − .084 .306 DBP .109 .058 − .182* .026 − .026 .753 Pulse .145* .012 .045 .588 − .031 .705 FBS .555** < 0.001 .010 .904 .065 .427 Serum Cholesterol .205** < 0.001 .086 .295 .070 .398 Triglycerides .126* .029 − .035 .671 .021 .797 LDL C .180** .002 − .022 .789 .072 .384 VLDL C .098 .089 .066 .425 − .068 .406 HDL C .036 .538 .109 .184 .070 .392 Allele and Genotype Frequency of Visfatin SNPs (rs2302559, rs1215113036) In patients with MetS and healthy persons, the allele and genotype frequencies of the visfatin gene and its SNPs were also determined. In patients with MetS compared to controls, the mutant genotype frequency, or CC, for the visfatin SNP rs2302559 was found to be 18-fold higher with 10.228–32.466 95%CI. However, compared to a normal person, MetS patients had a significantly (p-value < 0.01) higher frequency of the mutant allele, C as mentioned in Table 5 . Amplification results of visfatin rs2302559 SNP showed the homozygous mutant (CC) with 383 and 147 bp and heterozygous (CT) genotypes with 383,294 and 147 bp demonstrated in Fig. 1 . Additionally, it was discovered that individuals with MetS had a 129-fold higher frequency with 44.576-375.693 95%CI of the mutant genotype, or GA, for the visfatin SNP rs1215113036 than did the controls. While the frequency of the mutant allele, A, was significantly (p-value < 0.01) higher in MetS patients when compared to controls as mentioned in Table 5 . However, amplification results of visfatin rs1215113036 SNP showed the wild type (GG) genotypes with 258 and 104 bp and heterozygous (GA) genotypes with 258, 208 and 104 bp demonstrated in Fig. 2 . Sequencing Analysis results also showed the complete aligning with the wild sequence of visfatin gene. Which confirms the proper amplification of the target sequence of the visfatin rs1215113036 (Fig. 3 A) and rs2302559 (Fig. 3 B). With some variations. Table 5 Allele and Genotype Frequency of Visfatin SNPs (rs2302559 and rs1215113036) Polymorphism Control Cases Chi square P value OR (95%CI), p-value Allele Frequencies Lower Upper rs2302559 n (%) n (%) n % Allelic Frequency T 123(41.0%) 30(10.0%) Reference 153 25.5 C 177(59.0%) 270(90.0%) 6.45 4.02 9.72 < 0.001 447 74.5 Genotypic Frequency TC 123(82.0%) 30(20.0%) 115.36 0.000 Reference CC 27(18.0%) 120(80.0%) 18.222 10.228 32.466 < 0.001 rs1215113036 Allelic Frequency G 267(89.0%) 150(50.0%) Reference 417 69.5 A 33(11.0%) 150(50.0%) 8.09 5.28 12.39 < 0.001 183 30.5 Genotypic Frequency GG 117(78.0%) 4(2.7%) 176.86 0.000 Reference GA 33(22.0%) 146(97.3%) 129.40 44.576 375.693 < 0.001 Discussion In the pathophysiology of the metabolic problems linked to visceral obesity, insulin resistance is a key player. Adipokines, chemical messengers released by adipocytes, are considered to influence insulin activity. Visfatin is a complex molecule with elevated circulation levels in metabolic diseases. In previous investigation, visfatin levels in obese MS cases has shown to be considerably greater than in controls and MetS individuals without obesity. Furthermore, visfatin serum concentration rises with rising BMI and is positively connected with waist circumference and lipid parameters. According to earlier research, increased levels of visfatin is found in MetS cases compared to controls(Berezin, 2016 ; Chang et al., 2011 ; Dakroub et al., 2021 ). It's interesting to note that visfatin not only has circulating levels that is closely correlated with visceral adiposity, but visfatin also has insulin-like effects that are mediated via the insulin receptor and reduced blood sugar levels in mice. Additionally, chronic visfatin treatment to mice has reduced plasma insulin and glucose levels. Preadipocytes has encouraged by visfatin to produce and accumulate triglycerides, and other adipokines, such as interleukin 6, and tumor necrosis factor. These results have established visfatin as a promising option for controlling the intricate interaction between visceral obesity and related metabolic problems. Therefore, it is proposed that interindividual variation in visceral obesity or insulin resistance may be influenced by genetic variations at the visfatin gene locus(Chang et al., 2011 ; Ezzati-Mobaser et al., 2020 ; Sommer et al., 2008 ; Sonoli et al., 2011 ; Stastny, Bienertova-Vasku, Vasku, Research, & Reviews, 2012 ; Younes et al., 2021 ). In general, MetS prevalence in morbid obesity increased in both sexes after the age of 54. Yet, after this age chances of the MetS increases dramatically in women as compared to men. Previous studies also showed that metabolic syndromes are influenced by the age of an individual’s irrespective to the gender of that person(Araki et al., 2008 ; Jin et al., 2008 ; Misra & Bhardwaj, 2014 ; Nourbakhsh, Nourbakhsh, Gholinejad, Razzaghy-Azar, & Investigation, 2015 ). According to earlier findings, people with a family history (FH) of hypertension has significantly higher rates of hypertension. In addition, the prevalence of central obesity, metabolic syndrome, and obesity are all linked to FH of hypertension. The FH of hypertension group is easy to identify and could benefit from specific interventions. These studies supported our findings which suggest the considerable association of metabolic syndrome with age of the individuals and positive family history of the patients(Gupta, Shah, Nayyar, & Misra, 2013 ). Previous findings revealed no significant differences between the genotypes of rs2302559 and anthropometric parameters (BMI, waist circumference, waist to height ratio, and fat mass)(Chen et al., 2007 ). Visfatin levels are not significantly different between rs2302559 genotypes in earlier study, but a study that has looked at 243 obese children found that rs2302559 variant allele carriers has lower fasting serum visfatin and lower fasting plasma glucose than wild-type allele carriers(Vasilache et al., 2020 ). However according to our findings, individuals with any genetic variation of visfatin i.e., rs2302559 and rs1215113036 has more susceptible to metabolic syndrome. Our research has also suggested that metabolic syndrome is also associated significantly with the physical characteristics including weight, BMI, blood pressure and pulse rate. Moreover, biochemicals such as serum visfatin, triglycerides, fasting blood sugar, serum cholesterol, LDL and VLDL cholesterol has also been found considerably in metabolic syndromic patients. Positive correlation of visfatin with age, BMI, blood pressure, fasting blood sugar, serum triglyceride and LDL cholesterol has been observed. In past studies majority of the studies suggested no significant association of visfatin variation with metabolic syndrome Conclusion Metabolic syndrome is characterized by multiple factors, including central obesity, dyslipidaemia, hypertension, and impaired glucose tolerance. Most significant aspect of metabolic syndrome is thought to be obesity. Influence of gene variants involved in the synthesis or action of these hormones could also explain the conflicting results when considering the relationship between visfatin plasmatic levels and obesity. However, there is a lot of debate and very little research on these polymorphisms. Even though the SNPs for visfatin rs2302559 and rs1215113036 were found to be linked to metabolic factors in this study, further research is needed to figure out how exactly the studied SNPs affect metabolic mechanism. In order to complete the profile of these polymorphisms and confirm the association at the populational level, additional genetic studies in larger study groups are required. Declarations Data Availability The article contains the image data that support the findings of this study, as confirmed by the authors. References Araki, S., Dobashi, K., Kubo, K., Kawagoe, R., Yamamoto, Y., Kawada, Y., . . . Shirahata, A. J. O. (2008). Plasma visfatin concentration as a surrogate marker for visceral fat accumulation in obese children. 16 (2), 384-388. Berezin, A. J. E. M. S. (2016). Does visfatin predict cardiovascular complications in metabolic syndrome patients. 5 (1000224), 2161-1017.1000224. Chang, Y. H., Chang, D. M., Lin, K. C., Shin, S. J., Lee, Y. J. J. 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Cite Share Download PDF Status: Published Journal Publication published 27 Dec, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 02 Aug, 2023 Reviews received at journal 07 Jul, 2023 Reviewers agreed at journal 29 Jun, 2023 Reviewers invited by journal 29 Jun, 2023 Editor assigned by journal 20 Jun, 2023 Editor invited by journal 11 Apr, 2023 Submission checks completed at journal 11 Apr, 2023 First submitted to journal 04 Apr, 2023 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. 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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-2775945","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":190717823,"identity":"853d9c87-a038-4790-9ddc-1499b9d35312","order_by":0,"name":"Sayyada Humaira Masood","email":"","orcid":"","institution":"University of Karachi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sayyada","middleName":"Humaira","lastName":"Masood","suffix":""},{"id":190717825,"identity":"629e7d1a-8c21-40bc-a13e-46664b03fcd3","order_by":1,"name":"Taseer Ahmed Khan","email":"","orcid":"","institution":"University of 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Naqvi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACdsYGKAvI+ACk2NgJaWGGaJEAaWGcAdLCTFALhJIAs3mQRHAC/mbmBqabbXfq+PsPt322+bVNno+ZgfHDxxzcWiQOMzYw57Y9k5A4cLB5dm7fbcM2ZgZmyZnb8FgD0XJYguFgYzNzbs9tRqAWNmZePFrkYVqAjGZmy57b9gS1GMC0GBwDamH4cTuRoBZDoJbDOecOS248w9jM2NtwO7mNmbEZr1/kjrc/fJxTdphf7vzxxww//ty2nd/efPDDR3zeB4IDcBZjG5hswK8eFfwhRfEoGAWjYBSMFAAAogROiL5WJEgAAAAASUVORK5CYII=","orcid":"","institution":"Isra University Karachi Campus","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Muntazir","lastName":"Naqvi","suffix":""},{"id":190717831,"identity":"ed662ff2-391d-425d-a4c9-3432a2719974","order_by":5,"name":"Mehru un Nisa Iqbal","email":"","orcid":"","institution":"University of Karachi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mehru","middleName":"un Nisa","lastName":"Iqbal","suffix":""}],"badges":[],"createdAt":"2023-04-04 10:14:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2775945/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2775945/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-48402-z","type":"published","date":"2023-12-27T15:01:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":35676856,"identity":"7b89f8c3-4da7-4457-9c2c-9a277d600feb","added_by":"auto","created_at":"2023-04-12 21:34:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative Gel Image of tARMS PCR of rs2302559\u003cbr\u003e\n \u003c/strong\u003e\u003cem\u003eL1=ladder, L2,L3,L4=controls ,L5,L6 and L7=patients\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2775945/v1/5a78e2cddcc6a40427f597d2.png"},{"id":35676857,"identity":"463a4d7a-2c82-4110-8eba-ea6f7b95c236","added_by":"auto","created_at":"2023-04-12 21:34:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127223,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative Gel Image of tARMS PCR of rs1215113036\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2775945/v1/c17bf37a4ccb9999c3580286.png"},{"id":35676855,"identity":"961197e1-1dfe-458f-ba1d-23b76d4dcbf2","added_by":"auto","created_at":"2023-04-12 21:34:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":108069,"visible":true,"origin":"","legend":"\u003cp\u003eSequence alignment with wild type sequence\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2775945/v1/ab1bc24bc80e92edda235819.png"},{"id":49028309,"identity":"bb079e6c-f209-4e2d-bb3c-b20e8f57e1ef","added_by":"auto","created_at":"2024-01-01 15:05:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":799801,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2775945/v1/eca755b1-b362-40ec-b262-4b773dc0db15.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Visfatin gene polymorphism with obesity related metabolic disorders among Pakistani population; a case control study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity is a global epidemic that is linked to a number of chronic diseases, including cancer, diabetes, heart disease, and hypertension, despite the fact that it can be avoided(Chooi, Ding, \u0026amp; Magkos, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wagner \u0026amp; Brath, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Gall bladder disease, insulin resistance, high blood pressure, sleep apnea, inflammation, dyspnea, non-alcoholic fatty liver disease, gestational diabetes mellitus, cancer, type 2 diabetes mellitus, and metabolic syndrome are among the comorbidities linked to obesity(Kushner \u0026amp; Kahan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiabetes, dyslipidaemia, high blood pressure, and abdominal obesity are all threats of metabolic syndromes. A high-calorie, high-cholesterol diet paired with a sedentary lifestyle, smoking, drinking alcohol, and gaining weight as an adult are all risk factors for metabolic syndrome, which is linked to obesity. Surprisingly, variations in lifestyle can affect the susceptibility to metabolic syndrome due to genetic variations in insulin resistance and fat distribution(Han \u0026amp; Lean, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mendrick et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe adipose tissue's release of different adipokines may be one of the main mechanisms underlying these lifestyle-related illnesses(Wnuk et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVisfatin, an adipokine released by adipose tissues, and it has been linked to obesity and inflammation in the blood. Visfatin performs various biological functions in the human body including immunomodulation, catalyzing distinct cellular functions and anti-apoptosis(Haddad, Nori, \u0026amp; Hamza, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Visfatin may be connected to the pathophysiology of diabetes and is essential for the establishment of the metabolic syndrome, according to previous study(Wnuk et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some SNPs for Visfatin that were discovered have been linked to problems associated with obesity and glucose/lipid metabolism. Additionally, improvements in insulin sensitivity and glucose tolerance have both been linked to increases in visfatin concentration in obese patients who have been trained in aerobic exercise. Therefore, Visfatin may be a candidate gene for changes in glucose and obesity-related phenotypes brought on by aerobic exercise training, and its gene polymorphisms may be the reason why different reactions to the same activities are observed in different people(Kaan et al.; Ooi, Ong, Heng, Loke, \u0026amp; Lee, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Younes, Ibrahim, Al-Jurf, \u0026amp; Zayed, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aim of the current study is to investigate the association of visfatin SNPs including rs2302559 and rs1215113036 with the obesity-related metabolic syndrome.\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cp\u003eThis study included 300 subjects of either sex, between the ages 30 to 60 years divided into two equal groups. Control group (n\u0026thinsp;=\u0026thinsp;150) had normal healthy individuals with BMI 18.5\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e, in Metabolic disorders group (n\u0026thinsp;=\u0026thinsp;150) included obese individual which had BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e with T2DM and other component of metabolic disorder. Subjects suffering from Chronic medical conditions like endocrinological problems, Cardiovascular and renal diseases were excluded from the study. A Written informed consent along with clinical history was sought from each subject. All cases were selected by non-probability purposive sampling technique.\u003c/p\u003e \u003cp\u003e6 ml fasting blood was collected from all subjects for Biochemical analysis and genetic analysis. Serum Visfatin by ELISA techniques, fasting blood glucose and lipid profile by kit method\u003c/p\u003e \u003cp\u003eGenomic DNA from whole blood were isolated using Kit Method. Visfatin gene primers were designed using online software Primer-1 \u0026reg;. Specificity of all designed primers were confirmed using online BLAST\u0026reg; program/software. Genotyping of Visfatin gene (rs2302559 and rs1215113036) were be performed by using T-ARMs PCR analysis. The fragments obtained were analyzed using agarose gel electrophoresis stained with ethidium bromide. The confirmation of sequence was done using direct DNA Sequencing and sequencing file were analysed at Mega11 software. Approval of the study was given by IRB of Dow University of Health Sciences (DUHS). (IRB-1969/DUHS/Approval /2021/353. The complete study carried out in compliance with the relevant guideline and regulation of the mentioned authority.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using SPSS software (SPSS Inc. version 20, Chicago, IL, USA). We evaluated the association of demographic variables between case and controls by Chi Square test and mean differences of continuous variables between case and controls by independent sample t-test. The relative associations between genotypes and cases and controls were assessed using to calculate odds ratios and 95% of confidence intervals. A two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003eDemographic Characteristics of MetS Patients and Controls\u003c/h2\u003e\n \u003cp\u003eDemographic information was gathered and collated from patients with the metabolic syndrome caused by obesity. MetS was significantly associated with age group in the context of gender and age groups. Among contrast, in individuals with HTN, obesity, and diabetes, MetS was found to be significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) linked with medical history. In individuals with metabolic syndrome in their mother\u0026apos;s family history, siblings, or at least one instance recorded in the family history, significant outcomes were found, as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic Characteristics of MetS Patients and Controls\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChi-Square test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge Years (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eMedical History\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e91.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e300.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e91.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDyslipidemia Hyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFamily History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e19.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003eMetS Association with General Physical Features\u003c/h2\u003e\n \u003cp\u003eWhen compared to persons with general physical characteristics, patients with metabolic syndrome had significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) higher levels of weight, body mass index, systolic and diastolic blood pressure, and pulse rate as shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMetS Association with General Physical Features\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003et-test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.07904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.8517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.0099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCircumferences (in)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.83145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePulse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003eMetS Patient\u0026rsquo;s Biochemical Analysis\u003c/h2\u003e\n \u003cp\u003eAdditionally, both samples\u0026mdash;cases and controls\u0026mdash;were subjected to a biochemical study. In patients with MetS compared to healthy people, biochemicals such serum visfatin, serum cholesterol, triglycerides, LDL and VLDL cholesterol, and fasting blood sugar were significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) higher as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBiochemicals Analysis of Patients with MetS\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003et-test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum Visfatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.13731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFasting Blood Sugar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.9667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.59617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231.1733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.91437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVLDL C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003eMetS Correlation with General physical and Biochemical Factors\u003c/h2\u003e\n \u003cp\u003eIn contrast to patients, results from Pearson correlation analysis indicated a significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) negative correlation between MetS and diastolic blood pressure in healthy individuals. Overall findings revealed a significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) positive correlation between visfatin and several variables, including age, weight, BMI, systolic blood pressure, pulse rate, fasting blood sugar, serum cholesterol, triglycerides, and LDL cholesterol as shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMetS Correlation with Various Factors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 14.6006%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" style=\"width: 18.641%;\"\u003e\n \u003cp\u003eOverall Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" style=\"width: 18.09%;\"\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 13.4619%;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eCorrelation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003ecorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003ecorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003ecorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eSerum Visfatin (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.275**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.294**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eBMI (Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.306**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eCircumferences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.253**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.182*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003ePulse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.145*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eFBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.555**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eSerum Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.205**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.126*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eLDL C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.180**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eVLDL C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.6006%;\"\u003e\n \u003cp\u003eHDL C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.4986%;\"\u003e\n \u003cp\u003e.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.775%;\"\u003e\n \u003cp\u003e.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 13.315%;\"\u003e\n \u003cp\u003e.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9587%;\"\u003e\n \u003cp\u003e.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003eAllele and Genotype Frequency of Visfatin SNPs (rs2302559, rs1215113036)\u003c/h2\u003e\n \u003cp\u003eIn patients with MetS and healthy persons, the allele and genotype frequencies of the visfatin gene and its SNPs were also determined. In patients with MetS compared to controls, the mutant genotype frequency, or CC, for the visfatin SNP rs2302559 was found to be 18-fold higher with 10.228\u0026ndash;32.466 95%CI. However, compared to a normal person, MetS patients had a significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) higher frequency of the mutant allele, C as mentioned in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eAmplification results of visfatin rs2302559 SNP showed the homozygous mutant (CC) with 383 and 147 bp and heterozygous (CT) genotypes with 383,294 and 147 bp demonstrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"Section3\" id=\"Sec9\"\u003e\n \u003cp\u003eAdditionally, it was discovered that individuals with MetS had a 129-fold higher frequency with 44.576-375.693 95%CI of the mutant genotype, or GA, for the visfatin SNP rs1215113036 than did the controls. While the frequency of the mutant allele, A, was significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) higher in MetS patients when compared to controls as mentioned in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eHowever, amplification results of visfatin rs1215113036 SNP showed the wild type (GG) genotypes with 258 and 104 bp and heterozygous (GA) genotypes with 258, 208 and 104 bp demonstrated in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eSequencing Analysis results also showed the complete aligning with the wild sequence of visfatin gene. Which confirms the proper amplification of the target sequence of the visfatin rs1215113036 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA) and rs2302559 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). With some variations.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAllele and Genotype Frequency of Visfatin SNPs (rs2302559 and rs1215113036)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003ePolymorphism\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eChi square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(95%CI),\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAllele Frequencies\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ers2302559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eAllelic Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e123(41.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e30(10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e177(59.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e270(90.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eGenotypic Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e123(82.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e30(20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e115.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e27(18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e120(80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ers1215113036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAllelic Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e267(89.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e150(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e33(11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e150(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGenotypic Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e117(78.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4(2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e176.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e33(22.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e146(97.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the pathophysiology of the metabolic problems linked to visceral obesity, insulin resistance is a key player. Adipokines, chemical messengers released by adipocytes, are considered to influence insulin activity. Visfatin is a complex molecule with elevated circulation levels in metabolic diseases. In previous investigation, visfatin levels in obese MS cases has shown to be considerably greater than in controls and MetS individuals without obesity. Furthermore, visfatin serum concentration rises with rising BMI and is positively connected with waist circumference and lipid parameters. According to earlier research, increased levels of visfatin is found in MetS cases compared to controls(Berezin, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chang et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Dakroub et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt's interesting to note that visfatin not only has circulating levels that is closely correlated with visceral adiposity, but visfatin also has insulin-like effects that are mediated via the insulin receptor and reduced blood sugar levels in mice. Additionally, chronic visfatin treatment to mice has reduced plasma insulin and glucose levels. Preadipocytes has encouraged by visfatin to produce and accumulate triglycerides, and other adipokines, such as interleukin 6, and tumor necrosis factor. These results have established visfatin as a promising option for controlling the intricate interaction between visceral obesity and related metabolic problems. Therefore, it is proposed that interindividual variation in visceral obesity or insulin resistance may be influenced by genetic variations at the visfatin gene locus(Chang et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ezzati-Mobaser et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sommer et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sonoli et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Stastny, Bienertova-Vasku, Vasku, Research, \u0026amp; Reviews, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Younes et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn general, MetS prevalence in morbid obesity increased in both sexes after the age of 54. Yet, after this age chances of the MetS increases dramatically in women as compared to men. Previous studies also showed that metabolic syndromes are influenced by the age of an individual\u0026rsquo;s irrespective to the gender of that person(Araki et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Jin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Misra \u0026amp; Bhardwaj, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Nourbakhsh, Nourbakhsh, Gholinejad, Razzaghy-Azar, \u0026amp; Investigation, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to earlier findings, people with a family history (FH) of hypertension has significantly higher rates of hypertension. In addition, the prevalence of central obesity, metabolic syndrome, and obesity are all linked to FH of hypertension. The FH of hypertension group is easy to identify and could benefit from specific interventions. These studies supported our findings which suggest the considerable association of metabolic syndrome with age of the individuals and positive family history of the patients(Gupta, Shah, Nayyar, \u0026amp; Misra, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious findings revealed no significant differences between the genotypes of rs2302559 and anthropometric parameters (BMI, waist circumference, waist to height ratio, and fat mass)(Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Visfatin levels are not significantly different between rs2302559 genotypes in earlier study, but a study that has looked at 243 obese children found that rs2302559 variant allele carriers has lower fasting serum visfatin and lower fasting plasma glucose than wild-type allele carriers(Vasilache et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However according to our findings, individuals with any genetic variation of visfatin i.e., rs2302559 and rs1215113036 has more susceptible to metabolic syndrome.\u003c/p\u003e \u003cp\u003eOur research has also suggested that metabolic syndrome is also associated significantly with the physical characteristics including weight, BMI, blood pressure and pulse rate. Moreover, biochemicals such as serum visfatin, triglycerides, fasting blood sugar, serum cholesterol, LDL and VLDL cholesterol has also been found considerably in metabolic syndromic patients. Positive correlation of visfatin with age, BMI, blood pressure, fasting blood sugar, serum triglyceride and LDL cholesterol has been observed.\u003c/p\u003e \u003cp\u003eIn past studies majority of the studies suggested no significant association of visfatin variation with metabolic syndrome\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMetabolic syndrome is characterized by multiple factors, including central obesity, dyslipidaemia, hypertension, and impaired glucose tolerance. Most significant aspect of metabolic syndrome is thought to be obesity. Influence of gene variants involved in the synthesis or action of these hormones could also explain the conflicting results when considering the relationship between visfatin plasmatic levels and obesity. However, there is a lot of debate and very little research on these polymorphisms. Even though the SNPs for visfatin rs2302559 and rs1215113036 were found to be linked to metabolic factors in this study, further research is needed to figure out how exactly the studied SNPs affect metabolic mechanism. In order to complete the profile of these polymorphisms and confirm the association at the populational level, additional genetic studies in larger study groups are required.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData Availability\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe article contains the image data that support the findings of this study, as confirmed by the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAraki, S., Dobashi, K., Kubo, K., Kawagoe, R., Yamamoto, Y., Kawada, Y., . . . Shirahata, A. J. O. (2008). Plasma visfatin concentration as a surrogate marker for visceral fat accumulation in obese children.\u003cem\u003e 16\u003c/em\u003e(2), 384-388.\u003c/li\u003e\n\u003cli\u003eBerezin, A. J. E. M. S. (2016). Does visfatin predict cardiovascular complications in metabolic syndrome patients.\u003cem\u003e 5\u003c/em\u003e(1000224), 2161-1017.1000224.\u003c/li\u003e\n\u003cli\u003eChang, Y. H., Chang, D. M., Lin, K. C., Shin, S. J., Lee, Y. J. J. D. m. r., \u0026amp; reviews. (2011). Visfatin in overweight/obesity, type 2 diabetes mellitus, insulin resistance, metabolic syndrome and cardiovascular diseases: a meta‐analysis and systemic review.\u003cem\u003e \u003c/em\u003e\u003cem\u003e27\u003c/em\u003e(6), 515-527. \u003c/li\u003e\n\u003cli\u003eChen, C.-C., Li, T.-C., Li, C.-I., Liu, C.-S., Lin, W.-Y., Wu, M.-T., . . . Lin, C.-C. J. M. (2007). The relationship between visfatin levels and anthropometric and metabolic parameters: association with cholesterol levels in women.\u003cem\u003e 56\u003c/em\u003e(9), 1216-1220.\u003c/li\u003e\n\u003cli\u003eChooi, Y. C., Ding, C., \u0026amp; Magkos, F. J. M. (2019). The epidemiology of obesity.\u003cem\u003e 92\u003c/em\u003e, 6-10.\u003c/li\u003e\n\u003cli\u003eDakroub, A., Nasser, S. A., Kobeissy, F., Yassine, H. M., Orekhov, A., Sharifi‐Rad, J., . . . Eid, A. H. J. J. o. C. P. (2021). Visfatin: An emerging adipocytokine bridging the gap in the evolution of cardiovascular diseases.\u003cem\u003e 236\u003c/em\u003e(9), 6282-6296.\u003c/li\u003e\n\u003cli\u003eEzzati-Mobaser, S., Malekpour-Dehkordi, Z., Nourbakhsh, M., Tavakoli-Yaraki, M., Ahmadpour, F., Golpour, P., \u0026amp; Nourbakhsh, M. J. C. (2020). The up-regulation of markers of adipose tissue fibrosis by visfatin in pre-adipocytes as well as obese children and adolescents.\u003cem\u003e 134\u003c/em\u003e, 155193.\u003c/li\u003e\n\u003cli\u003eGupta, N., Shah, P., Nayyar, S., \u0026amp; Misra, A. J. T. I. J. o. P. (2013). 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(2014). Obesity and the metabolic syndrome in developing countries: focus on South Asians. In \u003cem\u003eInternational nutrition: achieving millennium goals and beyond\u003c/em\u003e (Vol. 78, pp. 133-140): Karger Publishers.\u003c/li\u003e\n\u003cli\u003eNourbakhsh, M., Nourbakhsh, M., Gholinejad, Z., Razzaghy-Azar, M. J. S. J. o. C., \u0026amp; Investigation, L. (2015). Visfatin in obese children and adolescents and its association with insulin resistance and metabolic syndrome.\u003cem\u003e 75\u003c/em\u003e(2), 183-188.\u003c/li\u003e\n\u003cli\u003eOoi, D. S. Q., Ong, S. G., Heng, C. K., Loke, K. Y., \u0026amp; Lee, Y. S. J. B. g. (2016). In-vitro function of upstream visfatin polymorphisms that are associated with adverse cardiometabolic parameters in obese children.\u003cem\u003e 17\u003c/em\u003e, 1-7.\u003c/li\u003e\n\u003cli\u003eSommer, G., Garten, A., Petzold, S., Beck-Sickinger, A. G., Bl\u0026uuml;her, M., Stumvoll, M., \u0026amp; Fasshauer, M. J. C. S. (2008). Visfatin/PBEF/Nampt: structure, regulation and potential function of a novel adipokine.\u003cem\u003e \u003c/em\u003e\u003cem\u003e115\u003c/em\u003e(1), 13-23. \u003c/li\u003e\n\u003cli\u003eSonoli, S., Shivprasad, S., Prasad, C., Patil, A., Desai, P., \u0026amp; Somannavar, M. J. E. R. M. P. S. (2011). Visfatin-a review.\u003cem\u003e 15\u003c/em\u003e(1), 9-14.\u003c/li\u003e\n\u003cli\u003eStastny, J., Bienertova-Vasku, J., Vasku, A. J. D., Research, M. S. C., \u0026amp; Reviews. (2012). Visfatin and its role in obesity development.\u003cem\u003e 6\u003c/em\u003e(2), 120-124.\u003c/li\u003e\n\u003cli\u003eVasilache, S. L., Mărginean, C. O., Boaghi, A., Pop, R.-M., Banescu, C., Moldovan, V. G., . . . Pascanu, I. M. J. R. R. d. M. d. L. V. (2020). Implications of visfatin genetic variants in the metabolic profile of the Romanian pediatric population.\u003cem\u003e 28\u003c/em\u003e(2).\u003c/li\u003e\n\u003cli\u003eWagner, K.-H., \u0026amp; Brath, H. J. P. m. (2012). A global view on the development of non communicable diseases.\u003cem\u003e 54\u003c/em\u003e, S38-S41.\u003c/li\u003e\n\u003cli\u003eWnuk, A., Stangret, A., Wątroba, M., Płatek, A. E., Skoda, M., Cendrowski, K., . . . Szukiewicz, D. J. O. R. (2020). Can adipokine visfatin be a novel marker of pregnancy‐related disorders in women with obesity? \u003cem\u003e, 21\u003c/em\u003e(7), e13022.\u003c/li\u003e\n\u003cli\u003eYounes, S., Ibrahim, A., Al-Jurf, R., \u0026amp; Zayed, H. J. I. J. o. O. (2021). Genetic polymorphisms associated with obesity in the Arab world: a systematic review.\u003cem\u003e 45\u003c/em\u003e(9), 1899-1913. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes, Hypertension, vastatin, genetic, variation","lastPublishedDoi":"10.21203/rs.3.rs-2775945/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2775945/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe metabolic syndrome is a group of factors including central obesity, dyslipidemia, hypertension, and impaired glucose tolerance, with obesity being the most significant aspect. The influence of gene variants involved in hormone synthesis or action could explain the conflicting results when considering the relationship between visfatin plasmatic levels and obesity. However, there is a lack of research on these polymorphisms. This study found that the SNPs for visfatin rs2302559 and rs1215113036 were statistically significantly associated with the metabolic factors at p value 0.001 and odd ratio suggested that there is a great chance of developing the disorder if these variations are present up to at 129.40. Frequency of the mutant alles were to be found in higher in frequency.\u003c/p\u003e","manuscriptTitle":"Association of Visfatin gene polymorphism with obesity related metabolic disorders among Pakistani population; a case control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-12 21:34:37","doi":"10.21203/rs.3.rs-2775945/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-08-02T04:48:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-07T20:38:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96c63140-9370-4c3f-847a-d3f9cfa3018d","date":"2023-06-29T13:05:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-06-29T11:37:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-20T08:47:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-04-11T05:52:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-04-11T05:34:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-04-04T10:08:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ebb5dedd-2dac-4581-b189-930149d7690f","owner":[],"postedDate":"April 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":20597172,"name":"Biological sciences/Biotechnology"},{"id":20597173,"name":"Biological sciences/Biological techniques/Bioinformatics"},{"id":20597174,"name":"Biological sciences/Biological techniques/Electrophysiology"},{"id":20597175,"name":"Biological sciences/Biological techniques/Genomic analysis"},{"id":20597176,"name":"Biological sciences/Biological techniques/Sequencing"}],"tags":[],"updatedAt":"2024-01-01T15:05:20+00:00","versionOfRecord":{"articleIdentity":"rs-2775945","link":"https://doi.org/10.1038/s41598-023-48402-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2023-12-27 15:01:13","publishedOnDateReadable":"December 27th, 2023"},"versionCreatedAt":"2023-04-12 21:34:37","video":"","vorDoi":"10.1038/s41598-023-48402-z","vorDoiUrl":"https://doi.org/10.1038/s41598-023-48402-z","workflowStages":[]},"version":"v1","identity":"rs-2775945","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2775945","identity":"rs-2775945","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}