Anahita Zakeri, Amir Ebadinejad, Maryam Rahmati, Amir Abbas Momenan, Mahtab Niroomand, Majid Valizadeh, Fereidoun Azizi, Fahimeh Ramezani Tehrani, Farhad Hosseinpanah
{"title":"多囊卵巢综合征妇女的身体成分分析：德黑兰血脂和血糖研究（TLGS）的横断面研究。","authors":"Anahita Zakeri, Amir Ebadinejad, Maryam Rahmati, Amir Abbas Momenan, Mahtab Niroomand, Majid Valizadeh, Fereidoun Azizi, Fahimeh Ramezani Tehrani, Farhad Hosseinpanah","doi":"10.1186/s12902-024-01783-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Obesity is associated with the development of polycystic ovarian syndrome (PCOS), a complex endocrine disorder. However, the correlation between body composition and PCOS in women has not been thoroughly investigated. This study aimed to examine body composition using bioelectrical impedance analysis (BIA) in women with and without PCOS in a population-based study within the Tehran Lipid and Glucose Study (TLGS).</p><p><strong>Methods: </strong>We conducted a cross-sectional study among non-menopausal women aged 18-45 who underwent BIA in phase VII of the TLGS. A total of 150 participants with PCOS and 240 without PCOS were included based on the Rotterdam criteria. Baseline demographic, anthropometric, laboratory, and body composition parameters were compared between the two groups.</p><p><strong>Results: </strong>The mean age was 33.7 ± 7.45 years in the PCOS group and 35.49 ± 7.05 years in the control group. The mean BMI was 27.0 ± 4.0 kg/m2 in the PCOS group and 27.1 ± 4.4 kg/m2 in the control group. No significant differences were found in body composition parameters between the two groups as assessed by BIA. Additionally, there were no correlations between body composition and hormone parameters in PCOS patients.</p><p><strong>Conclusion: </strong>In this sample of non-referral patients with PCOS, the use of BIA did not provide added value beyond conventional anthropometric measures for assessing body composition. Further longitudinal research is needed to determine whether body composition analysis can enhance PCOS evaluation.</p>","PeriodicalId":9152,"journal":{"name":"BMC Endocrine Disorders","volume":"24 1","pages":"251"},"PeriodicalIF":2.8000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575009/pdf/","citationCount":"0","resultStr":"{\"title\":\"Body composition analysis in women with polycystic ovary syndrome: a cross-sectional study from the Tehran Lipid and Glucose Study (TLGS).\",\"authors\":\"Anahita Zakeri, Amir Ebadinejad, Maryam Rahmati, Amir Abbas Momenan, Mahtab Niroomand, Majid Valizadeh, Fereidoun Azizi, Fahimeh Ramezani Tehrani, Farhad Hosseinpanah\",\"doi\":\"10.1186/s12902-024-01783-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Obesity is associated with the development of polycystic ovarian syndrome (PCOS), a complex endocrine disorder. However, the correlation between body composition and PCOS in women has not been thoroughly investigated. This study aimed to examine body composition using bioelectrical impedance analysis (BIA) in women with and without PCOS in a population-based study within the Tehran Lipid and Glucose Study (TLGS).</p><p><strong>Methods: </strong>We conducted a cross-sectional study among non-menopausal women aged 18-45 who underwent BIA in phase VII of the TLGS. A total of 150 participants with PCOS and 240 without PCOS were included based on the Rotterdam criteria. Baseline demographic, anthropometric, laboratory, and body composition parameters were compared between the two groups.</p><p><strong>Results: </strong>The mean age was 33.7 ± 7.45 years in the PCOS group and 35.49 ± 7.05 years in the control group. The mean BMI was 27.0 ± 4.0 kg/m2 in the PCOS group and 27.1 ± 4.4 kg/m2 in the control group. No significant differences were found in body composition parameters between the two groups as assessed by BIA. Additionally, there were no correlations between body composition and hormone parameters in PCOS patients.</p><p><strong>Conclusion: </strong>In this sample of non-referral patients with PCOS, the use of BIA did not provide added value beyond conventional anthropometric measures for assessing body composition. 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Body composition analysis in women with polycystic ovary syndrome: a cross-sectional study from the Tehran Lipid and Glucose Study (TLGS).
Background: Obesity is associated with the development of polycystic ovarian syndrome (PCOS), a complex endocrine disorder. However, the correlation between body composition and PCOS in women has not been thoroughly investigated. This study aimed to examine body composition using bioelectrical impedance analysis (BIA) in women with and without PCOS in a population-based study within the Tehran Lipid and Glucose Study (TLGS).
Methods: We conducted a cross-sectional study among non-menopausal women aged 18-45 who underwent BIA in phase VII of the TLGS. A total of 150 participants with PCOS and 240 without PCOS were included based on the Rotterdam criteria. Baseline demographic, anthropometric, laboratory, and body composition parameters were compared between the two groups.
Results: The mean age was 33.7 ± 7.45 years in the PCOS group and 35.49 ± 7.05 years in the control group. The mean BMI was 27.0 ± 4.0 kg/m2 in the PCOS group and 27.1 ± 4.4 kg/m2 in the control group. No significant differences were found in body composition parameters between the two groups as assessed by BIA. Additionally, there were no correlations between body composition and hormone parameters in PCOS patients.
Conclusion: In this sample of non-referral patients with PCOS, the use of BIA did not provide added value beyond conventional anthropometric measures for assessing body composition. Further longitudinal research is needed to determine whether body composition analysis can enhance PCOS evaluation.
期刊介绍：
BMC Endocrine Disorders is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of endocrine disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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