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dc.contributor.authorYiğit, Şafak
dc.contributor.authorAkıncı, Buket
dc.contributor.authorÜlker Ekşi, Büşra
dc.contributor.authorKorkmaz Dayıcan, Damla
dc.contributor.authorÇalıkoğlu, Fulya
dc.contributor.authorÇelik, Yusuf
dc.contributor.authorYeldan, İpek
dc.contributor.authorSatman, İlhan
dc.date.accessioned2024-07-23T11:13:27Z
dc.date.available2024-07-23T11:13:27Z
dc.date.issued2024en_US
dc.identifier.citationYiğit, Ş., Akıncı, B., Ekşi, B. Ü., Dayıcan, D. K., Çalıkoğlu, F., Çelik, Y., Yeldan, İ., & Satman, İ. (2024). Using Cluster Analysis to Identify Metabolic Syndrome Components and Physical Fitness in Patients with Metabolic Syndrome. Metabolic syndrome and related disorders, 10.1089/met.2024.0041. Advance online publication. https://doi.org/10.1089/met.2024.0041en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12941/220
dc.description.abstractBackground: Metabolic syndrome (MetS) comprises a cluster of cardiovascular risk factors. Physical inactivity and reduced physical fitness are associated with one or more components of MetS. However, MetS has many components, and the unclear relationship between the components and physical fitness parameters can provide a plain and straightforward understanding of the clustering method.Aim: To identify the relationship between physical fitness parameters, physical activity levels, and components of MetS using hierarchical cluster analysis.Methods: One hundred twenty-one patients (mean age = 51.4 +/- 7.1/years, F:90, M:31) who were diagnosed as having MetS according to the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) criteria were included in the study. Fasting plasma glucose (FPG), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) were analyzed. Systolic and diastolic blood pressures, (SBP and DBP), were evaluated. Body composition (waist and hip circumference, (WC and HC), waist-to-hip ratio (WHR), body mass index (BMI), percent body fat, and visceral fat), upper and lower extremity muscle strength (dynamometer), and functional exercise capacity [6-minute walk test (6MWT)] were assessed as physical fitness parameters. Physical activity levels were assessed using a pedometer and number of steps (NS) was determined.Results: Of the patients, 45.5% were diagnosed as having MetS based on four components. The dendrogram consisted of two main clusters and four subclusters. The main cluster I composed of BMI, HC, WC, visceral fat, HDL-C, percent fat, SBP, DBP, and percent quadriceps. The main cluster II comprised FPG, TG, WHR, handgrip strength, 6MWT, and NS.Conclusion: MetS components clustered with different physical fitness parameters. The clusters in the dendrogram can provide substantial implications for heterogeneous MetS components and physical fitness parameters. Future studies are needed to elucidate the effectiveness of dendrogram-derived exercise programs in MetS.en_US
dc.language.isoengen_US
dc.relation.isversionof10.1089/met.2024.0041en_US
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_US
dc.subjectKüme analizien_US
dc.subjectCluster analysisen_US
dc.subjectMetabolik sendromen_US
dc.subjectMetabolic syndromeen_US
dc.subjectFiziksel aktiviteen_US
dc.subjectPhysical activityen_US
dc.subjectFiziksel uygunluken_US
dc.subjectPhysical fitnessen_US
dc.titleUsing Cluster Analysis to Identify Metabolic Syndrome Components and Physical Fitness in Patients with Metabolic Syndromeen_US
dc.typearticleen_US
dc.departmentMeslek Yüksekokulları, Meslek Yüksekokulu, Fizyoterapi Programıen_US
dc.contributor.institutionauthorAkıncı, Buket
dc.relation.journalMetab Syndr Relat Disorden_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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