Literature DB >> 34717706

Association between serum estradiol level, sex hormone binding globulin level, and bone mineral density in middle-aged postmenopausal women.

Zhongxin Zhu1,2, Jin Zhao3, Yanfei Fang3, Rongwei Hua4.   

Abstract

BACKGROUND: Changes in sex hormones are thought to play an important role in bone health in postmenopausal women. Our aim in this study was to evaluate the association between levels of estradiol (E2), which is the most potent endogenous estrogen, and sex hormone binding globulin (SHBG) and bone mineral density (BMD) among postmenopausal women, 40-59 years of age.
METHODS: Using data from the National Health and Nutrition Examination Survey 2013-2016, we performed weighted multivariable linear regression models to evaluate the associations between serum levels of E2 and SHBG and lumbar BMD. A weighted generalized additive model and smooth curve fitting were used to address potential nonlinearity.
RESULTS: A total of 608 postmenopausal women were included in the analysis. The serum E2 level was positively associated with lumbar BMD, after adjusting for other covariates (β 0.65; 95% confidence interval (CI) 0.38-0.93). An inverted U-shaped association between the serum E2 level and lumbar BMD was further identified, with the point of inflection at an E2 level of 70 pg/mL. There was no significant association between the SHBG level and lumbar BMD (β 0.01; 95% CI - 0.30 to 0.31). However, the association between these two variables was U-shaped, with the point of inflection at an SHBG level of 65 nmol/L.
CONCLUSIONS: Based on our findings, it may be beneficial to appropriately increase serum E2 levels to promote bone health in postmenopausal women with low estrogen levels. Considering the inverted U-shaped association, an excessive E2 level may be harmful to BMD. In addition, increasing the SHBG level to within the normal range (65-144 nmol/L) may be considered.
© 2021. The Author(s).

Entities:  

Keywords:  Bone health; Estrogen; Postmenopausal women; Sex hormone binding globulin; Sex hormones

Mesh:

Substances:

Year:  2021        PMID: 34717706      PMCID: PMC8557509          DOI: 10.1186/s13018-021-02799-3

Source DB:  PubMed          Journal:  J Orthop Surg Res        ISSN: 1749-799X            Impact factor:   2.359


Background

Osteoporosis is a common systemic musculoskeletal disorder associated with aging, which results in increased disability, mortality, and health-care costs; as such, osteoporosis is a serious public health issue worldwide [1-3]. Postmenopausal osteoporosis, which is the most common type of primary osteoporosis, is mainly caused by an aging-related estrogen deficiency and is associated with a high socioeconomic burden [4, 5]. Bone is highly dynamic, with continuous processes of ossification and resorption to maintain tissue homeostasis [6]. Changes in sex hormones play an important role in bone health among postmenopausal women, with estrogen deficiency after menopause negatively impacting bone remodeling via skeletal and extraskeletal mechanisms [7, 8]. Estrogen deficiency stimulates osteoclast activity by increasing the release of bone-resorbing cytokines, with a rapid bone loss in the early years after menopause and the rate slowing with advancing age [9]. Hormone replacement therapy is one of the treatments used to prevent osteoporosis in postmenopausal, generally being recommended for postmenopausal women under the age of 60 years [10]. Sex hormone-binding globulin (SHBG), which is produced and secreted by the liver, binds sex steroids to regulate their bioavailability in the bloodstream and is another important sex hormone involved in age-related bone health [11, 12]. To date, however, studies have evaluated the association between SHBG and bone mineral density (BMD) among males, with an inverse association between these two variables having been identified [13-15]. Our aim in this study was to evaluate the association between BMD and serum levels of estradiol (E2), which is the most potent endogenous estrogen, and SHBG, among postmenopausal women 40–59 years of age, using a population-based database.

Materials and methods

Data source and study population

The National Health and Nutrition Examination Survey (NHANES) is a large, ongoing cross-sectional survey designed to provide objective data on health conditions and address emerging public health issues in the general population in the United States. The survey protocols were approved by the Institutional Review Board of the National Center for Health Statistics, and all participants entered in the NHANES provided consent for the data to be used for research. Data from the NHANES between 2013 and 2016 were pooled for this study. The study population was restricted to postmenopausal women, 40–59 years of age. Among the 2040 eligible women, we excluded 840 who reported having a regular period in the past 12 months, 287 with an unrecorded menopausal status, 69 with missing serum E2 level data, 66 with missing SHBG level data, 105 with missing lumbar BMD data, and 65 who had a cancer diagnosis. Ultimately, 608 participants were included in the analysis.

Study variables

Serum E2 levels were measured using isotope dilution liquid chromatography tandem mass spectrometry, based on the reference method of the National Institute for Standards and Technology. SHBG levels were quantified by immuno-antibodies and chemiluminescence measurements. Lumbar BMD was quantified using dual-energy X-ray absorptiometry scans acquired on the Hologic Discovery model A densitometers. Multivariate models contain covariates that might confound the associations between serum E2, SHBG levels and lumbar BMD The covariates included in this study were age, race, educational level, body mass index, ratio of family income to poverty, moderate activities, smoking at least 100 cigarettes over the life period to the point of data capture, having ≥ 12 alcohol drinks per year over the life period to the point of data capture, blood urea nitrogen, serum uric acid, serum phosphorus, and serum calcium. The detailed process of these variables can be found on the NHANES website (https://www.cdc.gov/nchs/nhanes/).

Statistical analyses

The study participants were stratified into quartiles according to serum E2 or SHBG levels. All analyses were performed using R software (version 3.4.3), and EmpowerStats software (http://www.empowerstats.com), with statistical significance set at P < 0.05. Weighted multivariable linear regression models were used to evaluate the association between serum E2 and SHBG levels and lumbar BMD. According to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement [16], we conducted three models: Model 1, no adjustment for covariates; Model 2, adjusted for age and race; and Model 3, adjusted for all covariates. A weighted generalized additive model and smooth curve fitting were used to address the potential nonlinearity. Two-piecewise linear regression models were applied to examine threshold effects when nonlinearity associations were found.

Results

Baseline characteristics of the 608 postmenopausal women included in our study sample, classified by quartiles of serum E2 and SHBG levels, are presented in Tables 1 and 2, respectively. As shown in Table 1, compared to the Q4 group, women with lower serum E2 levels were older and had a lower lumbar BMD. In Table 2, the distribution of age was similar in the different SHBG level groups (P > 0.05). Women in the Q3 group of SHBG level had the lowest lumbar BMD.
Table 1

Weighted characteristics of study population based on serum estradiol level quartiles

Serum estradiol level (pg/mL)Q1(≤ 4.04)Q2(4.08–7.58)Q3(7.59–16.10)Q4(≥ 16.30)P value
Age (years)54.0 ± 4.353.8 ± 4.153.2 ± 4.450.2 ± 5.0 < 0.001
Race/Ethnicity (%)0.631
Non-Hispanic White61.469.968.970.4
Non-Hispanic Black11.312.812.112.1
Mexican American10.18.37.55.8
Other race/ethnicity17.19.011.511.7
Education level (%)0.018
Less than high school18.316.012.210.0
High school27.316.129.320.9
More than high school54.467.958.569.1
Body mass index (kg/m2)26.4 ± 5.629.9 ± 5.833.4 ± 6.132.2 ± 8.5 < 0.001
Income to poverty ratio2.9 ± 1.83.2 ± 1.73.2 ± 1.53.4 ± 1.60.096
Moderate activities (%)0.942
Yes42.745.346.344.8
No57.354.753.755.2
Smoked at least 100 cigarettes in life (%)0.018
Yes40.348.946.433.1
No59.751.153.666.9
Had at least 12 alcohol drinks in a year (%)0.174
Yes68.275.065.672.5
No31.825.034.427.5
Blood urea nitrogen (mg/dL)5.0 ± 1.65.1 ± 1.84.9 ± 1.84.6 ± 1.20.036
Serum uric acid (umol/L)269.4 ± 68.0297.3 ± 72.0301.1 ± 72.5291.6 ± 60.9 < 0.001
Serum phosphorus (mg/dL)1.3 ± 0.21.3 ± 0.11.2 ± 0.21.2 ± 0.2 < 0.001
Serum calcium (mg/dL)2.4 ± 0.12.4 ± 0.12.4 ± 0.12.3 ± 0.10.005
Sex hormone binding globulin (nmol/L)75.6 ± 36.661.7 ± 45.749.6 ± 31.670.6 ± 43.2 < 0.001
Lumbar bone mineral density (mg/cm2)936.2 ± 138.8964.3 ± 137.3994.4 ± 136.61051.7 ± 141.0 < 0.001

Mean ± SD for continuous variables: P value was calculated by weighted linear regression model. % for Categorical variables: P value was calculated by weighted chi-square test

Table 2

Weighted characteristics of study population based on sex hormone binding globulin quartiles

Sex hormone binding globulin level (nmol/L)Q1(≤ 35.96)Q2(35.97–52.0)Q3(52.08–75.52)Q4(≥ 76.23)P value
Age (years)51.9 ± 5.053.0 ± 5.052.8 ± 4.652.9 ± 4.50.200
Race/Ethnicity (%)0.008
Non-Hispanic White67.858.462.679.5
Non-Hispanic Black10.016.413.59.4
Mexican American8.911.88.03.7
Other race/ethnicity13.313.515.97.3
Education level (%)0.006
Less than high school10.516.418.211.0
High school30.427.920.916.1
More than high school59.155.760.972.9
Body mass index (kg/m2)34.6 ± 7.031.5 ± 6.430.4 ± 7.327.2 ± 6.0 < 0.001
Income to poverty ratio3.0 ± 1.53.1 ± 1.63.3 ± 1.83.2 ± 1.70.309
Moderate activities (%)0.099
Yes39.945.640.451.9
No60.154.459.648.1
Smoked at least 100 cigarettes in life (%)0.116
Yes41.642.834.547.6
No58.457.265.552.4
Had at least 12 alcohol drinks in a year (%) < 0.001
Yes57.575.962.583.2
No42.524.137.516.8
Blood urea nitrogen (mg/dL)5.1 ± 1.64.9 ± 1.54.7 ± 1.64.9 ± 1.70.107
Serum uric acid (umol/L)324.0 ± 64.1301.5 ± 68.3285.1 ± 62.8260.8 ± 65.2 < 0.001
Serum phosphorus (mg/dL)1.2 ± 0.21.2 ± 0.21.2 ± 0.21.3 ± 0.10.035
Serum calcium (mg/dL)2.4 ± 0.12.4 ± 0.12.4 ± 0.12.4 ± 0.10.517
Serum estradiol level (pg/mL)23.6 ± 37.717.8 ± 37.227.1 ± 45.928.6 ± 48.20.140
Lumbar bone mineral density (mg/cm2)1011.6 ± 137.4998.2 ± 156.4962.9 ± 140.6991.4 ± 142.60.032

Mean ± SD for continuous variables: P value was calculated by weighted linear regression model. % for Categorical variables: P value was calculated by weighted chi-square test

Weighted characteristics of study population based on serum estradiol level quartiles Mean ± SD for continuous variables: P value was calculated by weighted linear regression model. % for Categorical variables: P value was calculated by weighted chi-square test Weighted characteristics of study population based on sex hormone binding globulin quartiles Mean ± SD for continuous variables: P value was calculated by weighted linear regression model. % for Categorical variables: P value was calculated by weighted chi-square test The association between serum E2 level and lumbar BMD was positive in all three regression models (Table 3): model 1 (β 0.80; 95% confidence interval (CI) 0.54–1.06); model 2 (β 0.71; 95% CI 0.44–0.98); model 3 (β 0.65; 95% CI 0.38–0.93). The P value was significant for all three models (P < 0.001). There was no significant association between the SHBG level and lumbar BMD, as follows (Table 4): model 1 (β − 0.05; 95% CI − 0.34 to 0.23); model 2 (β − 0.13; 95% CI − 0.41 to 0.14); model 3 (β 0.01; 95% CI − 0.30 to 0.31). The P values for these regressions were not significant.
Table 3

Association between serum estradiol level (pg/mL) and lumbar bone mineral density (mg/cm2)

Model 1β (95% CI)Model 2β (95% CI)Model 3β (95% CI)
Serum estradiol level0.80 (0.54, 1.06)***0.71 (0.44, 0.98)***0.65 (0.38, 0.93)***
Serum estradiol level categories
Q1ReferenceReferenceReference
Q228.09 (− 4.47, 60.66)22.62 (− 9.46, 54.70)9.48 (− 23.99, 42.96)
Q358.25 (25.96, 90.53)53.09 (21.27, 84.92)36.44 (1.96, 70.93)
Q4115.57 (84.12, 147.02)107.49 (75.11, 139.86)90.98 (55.80, 126.16)
P for trend < 0.001 < 0.001 < 0.001

Model 1: no covariates were adjusted. Model 2: age, and race were adjusted. Model 3: age, race, educational level, body mass index, ratio of family income to poverty, moderate activities, smoked at least 100 cigarettes in life, had at least 12 alcohol drinks in a year, blood urea nitrogen, serum uric acid, serum phosphorus, and serum calcium were adjusted

*P < 0.05; **P < 0.01; ***P < 0.001

Table 4

Association between sex hormone binding globulin level (nmol/L) and lumbar bone mineral density (mg/cm2)

Model 1β (95% CI)Model 2β (95% CI)Model 3β (95% CI)
Sex hormone binding globulin− 0.05 (− 0.34, 0.23)− 0.13 (− 0.41, 0.14)0.01 (− 0.30, 0.31)
Sex hormone binding globulin categories
Q1ReferenceReferenceReference
Q2− 13.36 (− 47.43, 20.71)− 9.25 (− 42.62, 24.12)− 15.19 (− 49.77, 19.40)
Q3− 48.66 (− 81.85, − 15.48)− 47.00 (− 79.39, − 14.62)− 42.37 (− 76.34, − 8.41)
Q4− 20.17 (− 51.88, 11.54)− 24.51 (− 55.61, 6.60)− 13.86 (− 49.23, 21.51)
P for trend0.0940.0390.260

Model 1: no covariates were adjusted. Model 2: age, and race were adjusted. Model 3: age, race, educational level, body mass index, ratio of family income to poverty, moderate activities, smoked at least 100 cigarettes in life, had at least 12 alcohol drinks in a year, blood urea nitrogen, serum uric acid, serum phosphorus, and serum calcium were adjusted

*P < 0.05; **P < 0.01; ***P < 0.001

Association between serum estradiol level (pg/mL) and lumbar bone mineral density (mg/cm2) Model 1: no covariates were adjusted. Model 2: age, and race were adjusted. Model 3: age, race, educational level, body mass index, ratio of family income to poverty, moderate activities, smoked at least 100 cigarettes in life, had at least 12 alcohol drinks in a year, blood urea nitrogen, serum uric acid, serum phosphorus, and serum calcium were adjusted *P < 0.05; **P < 0.01; ***P < 0.001 Association between sex hormone binding globulin level (nmol/L) and lumbar bone mineral density (mg/cm2) Model 1: no covariates were adjusted. Model 2: age, and race were adjusted. Model 3: age, race, educational level, body mass index, ratio of family income to poverty, moderate activities, smoked at least 100 cigarettes in life, had at least 12 alcohol drinks in a year, blood urea nitrogen, serum uric acid, serum phosphorus, and serum calcium were adjusted *P < 0.05; **P < 0.01; ***P < 0.001 The nonlinear relationship between serum levels of E2 and SHBG and lumbar BMD is shown in Figs. 1 and 2, respectively. Using a two-piecewise linear regression model, the point of inflection in the inverted U-shaped or U-shaped association between measured serum levels and lumbar BMD was at a level of 70 pg/mL for E2 and 65 nmol/L for SHBG (Table 5).
Fig. 1

The associations between serum estradiol level and lumbar bone mineral density. a Each black point represents a sample. b Solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. Adjusted for age, race, educational level, body mass index, ratio of family income to poverty, moderate activities, smoked at least 100 cigarettes in life, had at least 12 alcohol drinks in a year, blood urea nitrogen, serum uric acid, serum phosphorus, and serum calcium

Fig. 2

The associations between SHBG level and lumbar bone mineral density. a Each black point represents a sample. b Solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. Adjusted for age, race, educational level, body mass index, ratio of family income to poverty, moderate activities, smoked at least 100 cigarettes in life, had at least 12 alcohol drinks in a year, blood urea nitrogen, serum uric acid, serum phosphorus, and serum calcium

Table 5

Threshold effect analysis of serum estradiol level and sex hormone binding globulin level on lumbar bone mineral density using two-piecewise linear regression model

Lumbar bone mineral densityAdjusted β (95% CI), P value
Serum estradiol level
Fitting by standard linear model0.65 (0.38, 0.93) < 0.001
Fitting by two-piecewise linear model
Inflection point70 (pg/mL)
Serum estradiol level < 70 (pg/mL)1.92 (1.25, 2.59)
Serum estradiol level > 70 (pg/mL)− 0.24 (− 0.75, 0.27)
Log likelihood ratio < 0.001
Sex hormone binding globulin
Fitting by standard linear model0.01 (− 0.30, 0.31) 0.968
Fitting by two-piecewise linear model
Inflection point65 (nmol/L)
Sex hormone binding globulin < 65 (nmol/L)− 0.95 (− 1.80, − 0.09) 0.030
Sex hormone binding globulin > 65 (nmol/L)0.33 (− 0.08, 0.73) 0.115
Log likelihood ratio0.016

Age, race, educational level, body mass index, ratio of family income to poverty, moderate activities, smoked at least 100 cigarettes in life, had at least 12 alcohol drinks in a year, blood urea nitrogen, serum uric acid, serum phosphorus, and serum calcium were adjusted

The associations between serum estradiol level and lumbar bone mineral density. a Each black point represents a sample. b Solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. Adjusted for age, race, educational level, body mass index, ratio of family income to poverty, moderate activities, smoked at least 100 cigarettes in life, had at least 12 alcohol drinks in a year, blood urea nitrogen, serum uric acid, serum phosphorus, and serum calcium The associations between SHBG level and lumbar bone mineral density. a Each black point represents a sample. b Solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. Adjusted for age, race, educational level, body mass index, ratio of family income to poverty, moderate activities, smoked at least 100 cigarettes in life, had at least 12 alcohol drinks in a year, blood urea nitrogen, serum uric acid, serum phosphorus, and serum calcium Threshold effect analysis of serum estradiol level and sex hormone binding globulin level on lumbar bone mineral density using two-piecewise linear regression model Age, race, educational level, body mass index, ratio of family income to poverty, moderate activities, smoked at least 100 cigarettes in life, had at least 12 alcohol drinks in a year, blood urea nitrogen, serum uric acid, serum phosphorus, and serum calcium were adjusted

Discussion

In our study sample, which was a national representation of middle-aged postmenopausal women, the serum E2 level was positively associated with lumbar BMD, with no significant association between the SHBG level and lumbar BMD. Of note, we identified an inverted U-shaped association between BMD and serum E2, with a U-shaped association between BMD and serum SHBG. A decline in E2 level has been recognized as the most critical hormonal regulator of the menopause-associated decrease in BMD [17]. A study from Spain reported a positive association between E2 levels and BMD [18]. In a study of 132 women with postmenopausal osteoporosis and 81 postmenopausal women without osteoporosis, serum concentrations of E2 were found to be significantly lower in the osteoporosis group, indicative of a positive correlation between E2 and BMD [7]. A recent genome-wide study provided further support of the effects of E2 on BMD in maintaining skeletal health in both men and women [19]. In contrast, we identified an inverted U-shaped association between BMD and serum E2 levels, with a point of inflection at 70 pg/mL. The inverted U-shape indicates that an excessive E2 level may be harmful to BMD. Further prospective intervention trials are warranted to confirm this conclusion. A previous study identified that a higher SHBG level may be a risk factor for osteoporosis [20]. Evidence from the Concord Health and Ageing in Men Project in Australia reported that increasing serum SHBG levels were significantly associated with lower hip BMD [13]. A cross-sectional study of 404 men ≥ 45 years of age in China reported an inverse relationship between SHBG levels and BMD [14]. An inverse relationship between the SHBG level and BMD was also reported in the Osteoporotic Fractures in Men study, which included 1500 community-dwelling older men [15]. As well, a cross-sectional study reported a negative association between serum SHBG levels and bone mass, measured using quantitative ultrasound, among 382 premenopausal women [21]. This body evidence indicates that SHBG might play an important role in the development of osteoporosis, although this association may be influenced by skeletal site and age [22]. In contrast, as for the relationship between E2 and lumbar BMD, we identified a U-shaped association between SHBG and lumbar BMD, with the point of inflection at 65 nmol/L. A previous study reported that a lower SHBG level is associated with several diseases, including liver disease, arthritis, polycystic ovarian syndrome, cancer, and cardiovascular disease [23]. Therefore, properly increasing SHBG levels within the normal range (65–144 nmol/L) may be considered. The NHANES data are collected following standardized protocols, which could assure the accuracy and consistency of our data and results. However, the limitations of our study should be acknowledged in the interpretation of our results. First, a causal inference cannot be derived due to the cross-sectional design of the NHANES survey. A large-scale cohort study is necessary to further strengthen our results. Second, the NHANES samples were only measured once, which could have led to potential bias. Therefore, multiple tests are recommended for future studies. Third, although we used a nationally representative sample in this study, the population was restricted to postmenopausal women, 40–59 years of age. Therefore, the conclusions in this study cannot be generalized to premenopausal women or edlerly women.

Conclusion

Our finding revealed an inverted U-shaped association between serum E2 levels and lumbar BMD, suggesting that it may be beneficial to appropriately increase serum E2 levels to promote bone health in postmenopausal women with low estrogen levels, and an excessive E2 level may be harmful to BMD. In addition, increasing the SHBG level to within the normal range (65–144 nmol/L) may be considered.
  23 in total

1.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

Authors:  Erik von Elm; Douglas G Altman; Matthias Egger; Stuart J Pocock; Peter C Gøtzsche; Jan P Vandenbroucke
Journal:  Lancet       Date:  2007-10-20       Impact factor: 79.321

2.  SHBG, Sex Steroids, and Kyphosis in Older Men: The MrOS Study.

Authors:  Gina N Woods; Mei-Hua Huang; Peggy M Cawthon; Gail A Laughlin; John T Schousboe; Corinne McDaniels-Davidson; Jane A Cauley; Eric Orwoll; Elizabeth Barrett-Connor; Deborah M Kado
Journal:  J Bone Miner Res       Date:  2016-11-03       Impact factor: 6.741

3.  Osteoporosis in men: a potential role for the sex hormone binding globulin.

Authors:  E Legrand; C Hedde; Y Gallois; I Degasne; F Boux de Casson; E Mathieu; M F Baslé; D Chappard; M Audran
Journal:  Bone       Date:  2001-07       Impact factor: 4.398

Review 4.  Effect of drugs on bone mineral density in postmenopausal osteoporosis: a Bayesian network meta-analysis.

Authors:  Filippo Migliorini; Nicola Maffulli; Giorgia Colarossi; Jörg Eschweiler; Markus Tingart; Marcel Betsch
Journal:  J Orthop Surg Res       Date:  2021-08-27       Impact factor: 2.359

5.  Progressive Temporal Change in Serum SHBG, But Not in Serum Testosterone or Estradiol, Is Associated With Bone Loss and Incident Fractures in Older Men: The Concord Health and Ageing in Men Project.

Authors:  Benjumin Hsu; Markus J Seibel; Robert G Cumming; Fiona M Blyth; Vasi Naganathan; Kerrin Bleicher; David G Le Couteur; Louise M Waite; David J Handelsman
Journal:  J Bone Miner Res       Date:  2016-07-26       Impact factor: 6.741

Review 6.  Potential of biomarkers during pharmacological therapy setting for postmenopausal osteoporosis: a systematic review.

Authors:  Filippo Migliorini; Nicola Maffulli; Filippo Spiezia; Giuseppe Maria Peretti; Markus Tingart; Riccardo Giorgino
Journal:  J Orthop Surg Res       Date:  2021-05-31       Impact factor: 2.359

7.  The 2017 hormone therapy position statement of The North American Menopause Society.

Authors: 
Journal:  Menopause       Date:  2017-07       Impact factor: 3.310

8.  Overall adjustment acupuncture for postmenopausal osteoporosis (PMOP): a study protocol for a randomized sham-controlled trial.

Authors:  Z Q Ren; Y F Wang; G F Ao; H X Chen; M Huang; M X Lai; H D Zhao; R Zhao
Journal:  Trials       Date:  2020-06-03       Impact factor: 2.279

9.  Correlations between bone turnover markers, serum magnesium and bone mass density in postmenopausal osteoporosis.

Authors:  Ovidiu Alexandru Mederle; Melania Balas; Sorin Dumitru Ioanoviciu; Camelia-Vidita Gurban; Anca Tudor; Claudia Borza
Journal:  Clin Interv Aging       Date:  2018-08-03       Impact factor: 4.458

10.  Genome-wide Association Study of Estradiol Levels and the Causal Effect of Estradiol on Bone Mineral Density.

Authors:  Daniel Schmitz; Weronica E Ek; Elin Berggren; Julia Höglund; Torgny Karlsson; Åsa Johansson
Journal:  J Clin Endocrinol Metab       Date:  2021-10-21       Impact factor: 5.958

View more
  3 in total

1.  Circulating platelet concentration is associated with bone mineral density in women.

Authors:  Wei-Chun Ma; Yu-Cheng Cheng; Wen-Jane Lee; Yu-Hsuan Li; I-Te Lee
Journal:  Arch Osteoporos       Date:  2022-03-07       Impact factor: 2.617

2.  Association of Sex Hormones and Sex Hormone-Binding Globulin Levels With Bone Mineral Density in Adolescents Aged 12-19 Years.

Authors:  Ke Xu; Yicheng Fu; Buzi Cao; Mingyi Zhao
Journal:  Front Endocrinol (Lausanne)       Date:  2022-05-20       Impact factor: 6.055

3.  Association between testosterone levels and bone mineral density in females aged 40-60 years from NHANES 2011-2016.

Authors:  Han Zhang; Kun Ma; Run-Min Li; Jia-Ni Li; Shan-Feng Gao; Lin-Na Ma
Journal:  Sci Rep       Date:  2022-09-30       Impact factor: 4.996

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.