4.6

CiteScore

2.2

Impact Factor
  • ISSN 1674-8301
  • CN 32-1810/R
Chun Yuan, Xiaoqiang Liu, Yundong Mao, Feiyang Diao, Yugui Cui, Jiayin Liu. Polycystic ovary syndrome patients with high BMI tend to have functional disorders of androgen excess: a prospective study[J]. The Journal of Biomedical Research, 2016, 30(3): 197-202. DOI: 10.7555/JBR.30.20140111
Citation: Chun Yuan, Xiaoqiang Liu, Yundong Mao, Feiyang Diao, Yugui Cui, Jiayin Liu. Polycystic ovary syndrome patients with high BMI tend to have functional disorders of androgen excess: a prospective study[J]. The Journal of Biomedical Research, 2016, 30(3): 197-202. DOI: 10.7555/JBR.30.20140111

Polycystic ovary syndrome patients with high BMI tend to have functional disorders of androgen excess: a prospective study

Funds: 

The Major State Basic Research Development Program of China (973 Program: No. 2012CB944902 and No. 2012CB944703), the National Natural Science Foundation of China (No. 30801236), and the PriorityAcademic Program Development of Jiangsu HigherEducation Institutions

More Information
  • Received Date: June 22, 2014
  • Revised Date: September 02, 2014
  • Biochemical or clinical changes of hyperandrogenism are important elements of polycystic ovary syndrome (PCOS). There is currently no consensus on the definition and diagnostic criteria of hyperandrogenism in PCOS. The aim of this study was to investigate the complex symptoms of hyperandrogenic disorders and the correlations between metabolism and hyperandrogenism in patients with PCOS from an outpatient reproductive medicine clinic in China. We conducted a case control study of 125 PCOS patients and 130 controls to evaluate differences in body mass index (BMI), total testosterone (TT), modified Ferriman–Gallwey hirsutism score, sex hormone binding globulin (SHBG), homeostasis model assessment-estimated insulin resistance (HOMA-IR) and free androgen index (FAI) between PCOS patients and controls and subgroups of PCOS. The prevalence of acne and hirsutism did not differ significantly between the hyperandrogenic and non-hyperandrogenic subgroup. Patients with signs of hyperandrogenism had significantly higher BMI (P < 0.05), but differences in TT, SHBG, FAI and waist/hip ratio were insignificant. The odds ratio of overweight was calculated for all PCOS patients. Our results suggest that PCOS patients with high BMI tend to have functional disorders of androgen excess; therefore, BMI may be a strong predictor of hyperandrogenism in PCOS.
  • Related Articles

    [1]Ziyu Zhao, Yi Zhou, Jinxing Guan, Yan Yan, Jing Zhao, Zhihang Peng, Feng Chen, Yang Zhao, Fang Shao. The relationship between compartment models and their stochastic counterparts: A comparative study with examples of the COVID-19 epidemic modeling[J]. The Journal of Biomedical Research, 2024, 38(2): 175-188. DOI: 10.7555/JBR.37.20230137
    [2]Liuhua Zhou, Jiateng Sun, Tongtong Yang, Sibo Wang, Tiankai Shan, Lingfeng Gu, Jiawen Chen, Tianwen Wei, Di Zhao, Chong Du, Yulin Bao, Hao Wang, Xiaohu Lu, Haoliang Sun, Meng Lv, Di Yang, Liansheng Wang. Improved methodology for efficient establishment of the myocardial ischemia-reperfusion model in pigs through the median thoracic incision[J]. The Journal of Biomedical Research, 2023, 37(4): 302-312. DOI: 10.7555/JBR.36.20220189
    [3]Zheyue Wang, Qi Tang, Bende Liu, Wenqing Zhang, Yufeng Chen, Ningfei Ji, Yan Peng, Xiaohui Yang, Daixun Cui, Weiyu Kong, Xiaojun Tang, Tingting Yang, Mingshun Zhang, Xinxia Chang, Jin Zhu, Mao Huang, Zhenqing Feng. A SARS-CoV-2 neutralizing antibody discovery by single cell sequencing and molecular modeling[J]. The Journal of Biomedical Research, 2023, 37(3): 166-178. DOI: 10.7555/JBR.36.20220221
    [4]Desaulniers Amy T., Cederberg Rebecca A., Carreiro Elizabeth P., Gurumurthy Channabasavaiah B., White Brett R.. A transgenic pig model expressing a CMV-ZsGreen1 reporter across an extensive array of tissues[J]. The Journal of Biomedical Research, 2021, 35(2): 163-173. DOI: 10.7555/JBR.34.20200111
    [5]Pan Wei, Miyazaki Yasuo, Tsumura Hideyo, Miyazaki Emi, Yang Wei. Identification of county-level health factors associated with COVID-19 mortality in the United States[J]. The Journal of Biomedical Research, 2020, 34(6): 437-445. DOI: 10.7555/JBR.34.20200129
    [6]Wang Le Yi, McKelvey George M., Wang Hong. Multi-outcome predictive modelling of anesthesia patients[J]. The Journal of Biomedical Research, 2019, 33(6): 430-434. DOI: 10.7555/JBR.33.20180088
    [7]Jiawei Liao, Wei Huang, George Liu. Animal models of coronary heart disease[J]. The Journal of Biomedical Research, 2017, 31(1): 3-10. DOI: 10.7555/JBR.30.20150051
    [8]Qin Wei, Yeping Bian, Fuchao Yu, Qiang Zhang, Guanghao Zhang, Yang Li, Songsong Song, Xiaomei Ren, Jiayi Tong. Chronic intermittent hypoxia induces cardiac inflammation and dysfunction in a rat obstructive sleep apnea model[J]. The Journal of Biomedical Research, 2016, 30(6): 490-495. DOI: 10.7555/JBR.30.20160110
    [9]Mei Ju, Kun Chen, Baozhu Chang, Heng Gu. UVA1 irradiation inhibits fibroblast proliferation and alleviates pathological changes of scleroderma in a mouse model[J]. The Journal of Biomedical Research, 2012, 26(2): 135-142. DOI: 10.1016/S1674-8301(12)60023-2
    [10]Mukta Rani, Manas R. Dikhit, Ganesh C Sahoo, Pradeep Das. Comparative domain modeling of human EGF-like module EMR2 and study of interaction of the fourth domain of EGF with chondroitin 4-sulphate[J]. The Journal of Biomedical Research, 2011, 25(2): 100-110. DOI: 10.1016/S1674-8301(11)60013-4
  • Cited by

    Periodical cited type(20)

    1. Zhao Z, Zhou Y, Guan J, et al. The relationship between compartment models and their stochastic counterparts: A comparative study with examples of the COVID-19 epidemic modeling. J Biomed Res, 2024, 38(2): 175-188. DOI:10.7555/JBR.37.20230137
    2. Shuttleworth JG, Lei CL, Whittaker DG, et al. Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics. Bull Math Biol, 2023, 86(1): 2. DOI:10.1007/s11538-023-01224-6
    3. Hu C. Emergency Protective Measures and Strategies of COVID-19: From Lifestyle to Traditional Chinese Medicine. Clin Complement Med Pharmacol, 2023, 3(3): 100089. DOI:10.1016/j.ccmp.2023.100089
    4. Olayiwola MO, Alaje AI, Olarewaju AY, et al. A caputo fractional order epidemic model for evaluating the effectiveness of high-risk quarantine and vaccination strategies on the spread of COVID-19. Healthc Anal (N Y), 2023, 3: 100179. DOI:10.1016/j.health.2023.100179
    5. Noroozi-Ghaleini E, Shaibani MJ. Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks. Heliyon, 2023, 9(2): e13672. DOI:10.1016/j.heliyon.2023.e13672
    6. Bekker R, Uit Het Broek M, Koole G. Modeling COVID-19 hospital admissions and occupancy in the Netherlands. Eur J Oper Res, 2023, 304(1): 207-218. DOI:10.1016/j.ejor.2021.12.044
    7. Najem S, Monni S, Hatoum R, et al. A framework for reconstructing transmission networks in infectious diseases. Appl Netw Sci, 2022, 7(1): 85. DOI:10.1007/s41109-022-00525-4
    8. McAndrew T, Codi A, Cambeiro J, et al. Chimeric forecasting: combining probabilistic predictions from computational models and human judgment. BMC Infect Dis, 2022, 22(1): 833. DOI:10.1186/s12879-022-07794-5
    9. Zhang W, Liu S, Osgood N, et al. Using simulation modelling and systems science to help contain COVID-19: A systematic review. Syst Res Behav Sci, 2022. DOI:10.1002/sres.2897. Online ahead of print
    10. Nixon K, Jindal S, Parker F, et al. An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation. Lancet Digit Health, 2022, 4(10): e738-e747. DOI:10.1016/S2589-7500(22)00148-0
    11. Zhang P, Feng K, Gong Y, et al. Usage of Compartmental Models in Predicting COVID-19 Outbreaks. AAPS J, 2022, 24(5): 98. DOI:10.1208/s12248-022-00743-9
    12. Guan J, Zhao Y, Wei Y, et al. Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges. Med Rev (2021), 2022, 2(1): 89-109. DOI:10.1515/mr-2021-0022
    13. Rizzo S, Catanese C, Puligheddu C, et al. CT evaluation of lung infiltrates in the two months preceding the Coronavirus disease 19 pandemic in Canton Ticino (Switzerland): were there suspicious cases before the official first case?. Radiol Med, 2022, 127(4): 360-368. DOI:10.1007/s11547-022-01466-9
    14. Cocucci TJ, Pulido M, Aparicio JP, et al. Inference in epidemiological agent-based models using ensemble-based data assimilation. PLoS One, 2022, 17(3): e0264892. DOI:10.1371/journal.pone.0264892
    15. Yu S, Cui S, Rui J, et al. Epidemiological Characteristics and Transmissibility for SARS-CoV-2 of Population Level and Cluster Level in a Chinese City. Front Public Health, 2022, 9: 799536. DOI:10.3389/fpubh.2021.799536
    16. Wang H, Moore JM, Small M, et al. Epidemic dynamics on higher-dimensional small world networks. Appl Math Comput, 2022, 421: 126911. DOI:10.1016/j.amc.2021.126911
    17. Wei Y, Sha F, Zhao Y, et al. Better modelling of infectious diseases: lessons from covid-19 in China. BMJ, 2021, 375: n2365. DOI:10.1136/bmj.n2365
    18. Safari A, Hosseini R, Mazinani M. A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction. J Biomed Inform, 2021, 123: 103920. DOI:10.1016/j.jbi.2021.103920
    19. Duarte P, Riveros-Perez E. Understanding the cycles of COVID-19 incidence: Principal Component Analysis and interaction of biological and socio-economic factors. Ann Med Surg (Lond), 2021, 66: 102437. DOI:10.1016/j.amsu.2021.102437
    20. Liu J, Zhou Y, Ye C, et al. The spatial transmission of SARS-CoV-2 in China under the prevention and control measures at the early outbreak. Arch Public Health, 2021, 79(1): 8. DOI:10.1186/s13690-021-00529-z

    Other cited types(0)

Catalog

    Article Metrics

    Article views (3253) PDF downloads (489) Cited by(20)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return