Welcome to Francis Academic Press

Frontiers in Medical Science Research, 2024, 6(4); doi: 10.25236/FMSR.2024.060407.

Construction and Validation of Predictive Model for Decreased Bone Density in Premature Ovarian Insufficiency

Author(s)

Dong Jiaqi1, Zhang Yanru2

Corresponding Author:
Zhang Yanru
Affiliation(s)

1School of Medicine, Henan Polytechnic University, Jiaozuo, China

2School of Medicine, Henan Polytechnic University, Jiaozuo, China

Abstract

Premature ovarian insufficiency (POI) is an endocrine disorder that affects women of childbearing age. It is characterized by premature ovarian failure, leading to decreased estrogen levels and increased gonadotropin levels. POI not only impacts reproductive health but is also associated with various long-term health issues, particularly osteoporosis due to decreased estrogen protection, making individuals more susceptible to developing osteoporosis. This study utilized a retrospective cohort study design, including patients diagnosed with POI at the First Affiliated Hospital of Henan Polytechnic University during a certain period. Through detailed medical history collection, bone density measurements, and assessment of other relevant factors, we analyzed potential risk factors associated with decreased bone density caused by POI. Multivariable logistic regression analysis was used to identify independent risk factors, upon which a predictive model was constructed. The results showed that a total of 198 POI patients were included in the study, among whom 54 (34.8%) had decreased bone density. Multivariable analysis revealed that age, parity, family history of osteoporosis, BMI, and regular physical exercise were significantly associated with decreased bone density. Based on these factors, we developed a predictive model and evaluated its predictive performance using the receiver operating characteristic (ROC) curve, with an area under the ROC curve of 0.953 (95% CI: 0.924–0.983), indicating good predictive accuracy.

Keywords

Premature ovarian insufficiency, decreased bone density, predictive model, risk factors

Cite This Paper

Dong Jiaqi, Zhang Yanru. Construction and Validation of Predictive Model for Decreased Bone Density in Premature Ovarian Insufficiency. Frontiers in Medical Science Research (2024), Vol. 6, Issue 4: 50-54. https://doi.org/10.25236/FMSR.2024.060407.

References

[1] Soen S, Fukunaga M, Sugimoto T, et al. Diagnostic criteria for primary osteoporosis: year 2012 revision. J Bone Miner Metab. 2013;31(3):247-257.

[2] Fan J, Jiang Y, Qiang J, et al. Associations of Fat Mass and Fat Distribution With Bone Mineral Density in Non-Obese Postmenopausal Chinese Women Over 60 Years Old[J]. Frontiers in Endocrinology, 2022, 13: 829867.

[3] Zhang Y, Tan C, Tan W. BMI, socioeconomic status, and bone mineral density in U.S. adults: Mediation analysis in the NHANES [J]. Frontiers in Nutrition, 2023, 10: 1132234.

[4] Aggarwal N, Raveendran A, Khandelwal N, et al. Prevalence and related risk factors of osteoporosis in peri- and postmenopausal Indian women. [J]. Midlife Health. 2011;2(2):81-85. 

[5] Martínez Pérez JA, Palacios S, García FC, Pérez M. Assessing osteoporosis risk factors in Spanish menopausal women[J].Gynecol Endocrinol. 2011;27(10):807-813.

[6] Schnatz PF, Marakovits KA, O'Sullivan DM. Assessment of postmenopausal women and significant risk factors for osteoporosis [J].Obstet Gynecol Surv. 2010;65(9):591-596.

[7] Pinar G, Kaplan S, Pinar T, et al. The prevalence and risk factors for osteoporosis among 18- to 49-year-old Turkish women [J].Women Health. 2017;57(9):1080-1097. 

[8] Zhang L, Luo X, Liu H, et al. Prevalence and risk factors of osteoporosis and osteopenia among residents in Hubei province, China[J].Arch Osteoporos. 2023;18(1):49. Published 2023 Apr 15. 

[9] Lee JH, Sung YK, Choi CB, et al. The frequency of and risk factors for osteoporosis in Korean patients with rheumatoid arthritis [J].BMC Musculoskelet Disord. 2016;17:98. Published 2016 Feb 24. 

[10] Bijelic R, Milicevic S, Balaban J. Risk Factors for Osteoporosis in Postmenopausal Women [J].Med Arch. 2017;71(1):25-28.