Skip to main content
NCBI home page
Medicine logoLink to Medicine
. 2025 Aug 29;104(35):e44153. doi: 10.1097/MD.0000000000044153

Impact of mild cortisol excess on osteoporosis and the mediating role of sarcopenia-related traits: A Mendelian randomization study

Junyu Zhu a, Shijie Zhou b, Mingqi Jiang c, Wenying Wang b, Jun Yan a,*
PMCID: PMC12401307  PMID: 40898539

Abstract

Studies show excessive cortisol is linked to osteoporosis (OP). However, the impact of mild cortisol excess (MCE) on bone mineral density (BMD) remains unclear. Sarcopenia may play a key role in this, particularly in aging or stress contexts. To investigate the association between MCE and OP outcomes and the proportion of this association that is mediated through SP using Mendelian randomization (MR). MR study using summary statistics with traits were obtained from publicly available genome-wide association studies (GWAS) of OP and sarcopenia-related traits. Three single-nucleotide polymorphisms (SNPs) associated with plasma cortisol concentrations in the CORtisol NETwork consortium were used as instrumental variables. All participants were of European ancestry. Random-effect inverse-variance weighted (IVW) method was used as the main analysis of MR, and a series of sensitivity analyses were performed to detect heterogeneity and horizontal pleiotropy. A 2-step MR approach was used to investigate whether the mediating pathway from MCE to OP was mediated by sarcopenia-related traits. Cortisol increases OP risk in the femoral neck (FN, OR = 0.874, 95% CI: 0.786–0.973, P = .0138), while reducing it in the lumbar spine (LS, OR = 1.140, 95% CI: 1.008–1.291, P = .037) and heel (OR = 1.027, 95% CI: 1.000–1.054, P = .047). The mediation analysis via 2-step MR showed that sarcopenia mediates up to 8.4% of the cortisol-induced osteoporosis risk in the LS. This MR analysis suggests that MCE primarily increases OP risk in the FN, rather than affecting the body systemically. It also shows that SP mediates up to 8.4% of the cortisol-induced OP risk in the lumbar spine, highlighting the importance of muscle-strengthening exercises in preventing OP.

Keywords: bone mineral density, cortisol, femoral neck, Mendelian randomization, osteoporosis, sarcopenia-related traits

1. Introduction

Osteoporosis (OP) is a multifactorial metabolic bone disorder characterized by a decrease in bone mineral density (BMD) and an increased risk of fractures.[1–4] Projections indicate that by 2025, OP will be responsible for approximately 3 million fractures annually in the United States (US). In the European Union (EU), it is estimated that the annual osteoporotic fracture rate will increase by 25% between 2019 and 2034, and with their aging population this is set to increase.[5,6]

It has been reported that the onset and progression of OP are closely related to the regulation of endocrine hormone secretion.[7–10] Cortisol, as a stress hormone, plays a crucial role in the onset and progression of OP and is closely linked to our daily lives.[11,12] Thus, elucidating the relationship between cortisol and OP has positive clinical significance for the prevention of OP.

It is well established that pathological levels of cortisol have been extensively studied and reported for their detrimental effects on bone density and bone tissue.[13] However, the relationship between mild cortisol excess (MCE) and OP remains controversial.

BMD measured by dual X-ray DXA is the current reference standard for the diagnosis of OP.[14] Some studies indicate that MCE may not necessarily result in reduced BMD, and that the decline in BMD is not directly proportional to the severity of the disease.[15] This stands in contrast to the conclusions drawn by other studies.

It may be attributed to other mediating factors. Research indicates that MCE may accelerate the progression of SP, a condition closely associated with the development of OP.[16–19] It suggests that SP may be one of the key mediating pathways through which cortisol impacts the development of OP. However, uncertainty remains concerning the causal influence of MCE on OP outcomes and the role of SP as a mediating pathway. Better characterization of the association between MCE and OP risk would have significant public health implications for reducing the burden of this disease.

Mendelian randomization (MR) is a valid approach for causal inference using genetic variants as instrument variables (IVs), which can effectively overcome the confounding bias of traditional epidemiological studies.[20] Our MR study had 2 primary aims: first, to assess the causal relationship between MCE and OP; And second, to evaluate the role of SP as a mediating pathway in the association between MCE and OP. Specifically, we primarily focused on BMD at 4 common osteoporotic fracture sites: lumbar spine BMD (LS-BMD), femoral neck BMD (FN-BMD), forearm BMD (FA-BMD), heel BMD (H-BMD) and total BMD (T-BMD).[21,22] For the mediating factors, we included sarcopenia-related traits.[23,24]

2. Methods

2.1. Study design

We used a 2-sample MR design: a genetic IV analysis based on summary-level data with SNPs as instruments for the risk factor. For causal estimates from MR studies to be valid, 3 assumptions must be adhered to: the genetic variants are highly associated with the exposure, the genetic variants are not associated with any potential confounder of the exposure–outcome association, and the variants exclusively affect the outcome through the exposure.[25] We first performed a 2-sample bidirectional MR to investigate the causal relationship of MCE (measured as cortisol) with OP (measured as BMD) risk. Subsequently, we utilized MR analysis to investigate the causal relationship between MCE and sarcopenia-related traits (measured as body lean mass and low grip strength). A 2-step MR analysis was conducted to examine whether SP mediated the relationship between MCE and OP, given the significant causal associations between MCE and SP, and their respective causal effects on OP (Fig. 1).

Figure 1.

Figure 1.

Open in a new tab

Schematic presentation of the Mendelian randomization design.

This study was reported in accordance with the guidelines of strengthening the reporting of observational studies in epidemiology using MR: the STROBE-MR statement.[26] And the summary-level data from genome-wide association studies (GWAS) were used to find genetic proxies of an exposure and investigate the associations of these proxies with an outcome. All studies had been approved by a relevant ethical review board and participants had given informed consent, which did not require institutional review board approval because of the public characteristics of the data of GWAS.

2.2. Instrumental variable selection for cortisol

Cortisol variants were drawn from a GWAS meta-analysis of 12,597 individuals in the CORtisol NETwork (CORNET) consortium, comprising 11 Western European population-based cohorts.[27] The study excluded participants using glucocorticoids, pregnant or breastfeeding women, and twins. Plasma cortisol concentrations were measured via immunoassay from blood samples collected between 7:00 and 11:00 AM (mean: 472 nmol/L). The data were standardized (mean = 0, SD = 1) and log-transformed for analysis.[27] Plasma cortisol concentrations were measured between 7:00 and 11:00 AM to align with the natural circadian rhythm of cortisol, which typically peaks in the early morning and gradually declines throughout the day. This standardized timing helps minimize inter-individual variability and reduces potential confounding from external factors that may influence cortisol levels later in the day. Based on recent MR studies of cortisol, we selected 3 SNPs independently associated with plasma cortisol concentrations at genome-wide significance (P < 5e−08, r2 < 0.3). The selection of 3 SNPs was guided by recent findings from GWAS. While incorporating a larger number of SNPs could potentially enhance precision, the 3 selected variants represent the most robust and independent instruments currently available. This approach is supported by prior MR studies and reflects a balance between maximizing the genetic explanatory power for cortisol levels and minimizing the risk of horizontal pleiotropy.[27,28] The variants rs12589136 and rs11621961 are located in the SERPINA6 gene, which encodes corticosteroid-binding globulin (CBG). The SNP rs2749527, located in the SERPINA1 gene encoding α1-antitrypsin, was adjusted for age, sex, and ancestry principal components, explaining 0.54% of the variation in plasma cortisol levels[28] (Table 1). The F-statistics for IVs suggested that genetic variants strongly predicted plasma cortisol concentrations (F-statistics > 10).[29]

Table 1.

Overview of genetic data sets.

Trait GWAS ID Year PMID No. sample size
Cortisol ieu-a-1012 2014 25010111 12,597
LS-BMD ieu-a-982 2015 26367794 28,498
FN-BMD ieu-a-980 2015 26367794 32,735
FA-BMD ieu-a-977 2015 26367794 8143
Heel-BMD ebi-a-GCST006979 2019 30598549 426,824
T-BMD ebi-a-GCST005348 2018 29304378 56,284
WBLM ukb-b-13354 2018 NA 454,850
ALM ebi-a-GCST90000025 2020 33097823 450,243
Right-hand grip ukb-b-10215 2018 NA 461,089
Right-hand grip ukb-b-7478 2018 NA 461,026
Open in a new tab

ALM = appendicular lean mass, FA-BMD = forearm bone mineral density, FN-BMD = femoral neck bone mineral density, GWAS ID = genome-wide association studies identity number, Heel-BMD = heel bone mineral density, LS-BMD = lumbar spine bone mineral density, NA = not applicable, PMID = PubMed identification number, T-BMD = total-bone mineral density, WBLM = whole body lean mass.

2.3. Outcomes

The GEnetic factors for osteoporosis consortium (GEFOS, http://www.gefos.org) is a large international cooperation organization, involving many famous research groups. LS-BMD, FN-BMD, and FA-BMD data were obtained from the 2015 GWAS summary statistics on BMD in a population of European ancestry.[21] The GWAS summary statistics for Heel-BMD from the UK Biobank (UKB).[30] 426,824 individuals of European ancestry identified SNPs associated with Heel-BMD. The GWAS summary statistics for T-BMD were derived from a 2018 meta-analysis, which included a population with 86% of European ancestry[22] (Table 1).

2.4. Mediators

For sarcopenia-related traits, we selected whole body lean mass (WBLM), appendicular lean mass (ALM), and grip strength. Grip strength, as a measure of muscle strength, and WBLM, as a measure of muscle mass, are both well-established predictors of sarcopenia. ALM, primarily determined by skeletal muscle, serves as an additional measure of muscle mass and enhances the predictive accuracy for SP when combined with other metrics. The GWAS summary statistics for hand grip strength were obtained from the UKB, which includes 461,089 United Kingdom (UK) individuals for right-hand grip strength, and 461,026 individuals for left-hand grip strength.[24] The GWAS summary statistics for WBLM (N = 454,850) from the UKB.[24] The ALM-related values were quantified by the sum of fat-free mass with 450,243 UKB cohort participants (https://www.ebi.ac.uk/gwas/publications/33097823)[23] (Table 1).

2.5. Statistical analysis

All analyses were run using R, version 4.3.2 (R program for statistical computing) and the R packages MR, version 0.10.0, and 2 sample MR, version 0.6.4. The inverse-variance weighted (IVW) method was used as the main approach in this MR analysis, which provides accurate estimates in the absence of heterogeneity and directional pleiotropy between the cortisol and SP.[31–33] The heterogeneity of the IVW model was assessed by Cochran Q test. If Cochran Q test suggested significant heterogeneity (P < .05), we turned from the fixed IVW model to the random-effects model. MR-Egger analysis was conducted to estimate the causal effect, with the intercept used to interpret and assess both directional and horizontal pleiotropy. A significant deviation of the intercept from zero (P < .05) suggests the presence of directional pleiotropy.[25,34] The odds ratio (OR) was used as the measure of association in the MR analysis. The OR reflects the change in the odds of osteoporosis per unit increase in plasma cortisol levels or sarcopenia-related traits. A significant OR would suggest a causal relationship, while a non-significant OR would imply no discernible causal effect of cortisol or sarcopenia on osteoporosis risk. The MR pleiotropy residual sum and outlier (MR-PRESSO) method was used to evaluate horizontal pleiotropy.[35] If horizontal pleiotropy is detected, it is corrected by removing outliers and examining whether significant differences in the causal estimates exist before and after outlier removal. In order to evaluate the stability of the results, an array of sensitivity analyses was executed. A Leave-one-out analysis was initiated to scrutinize whether a single SNP was the driving force behind the causal signal.[36]

2.6. Mediation analysis

If the methods above confirm a causal relationship between cortisol and OP without evidence of directional pleiotropy, a 2-step MR approach will be employed. This method will assess the mediating role of SP in the causal pathway between the exposure (cortisol) and the outcome (OP), while estimating the magnitude of the mediation effect.[37]

3. Results

Based on the GWAS of European ancestry, we identified a total of 3 independent SNPs that were associated with the plasma cortisol concentrations and OP, as well as 3 independent SNPs associated with the cortisol and SP.

3.1. The causal effect of cortisol on OP

In IVW MR analysis, each 1 SD increase in plasma cortisol concentrations was associated with increased risk OP in the FN region (OR = 0.874, 95% CI: 0.786–0.973, P = .014), contrasted with a decreased risk in the LS (OR = 1.140, 95% CI: 1.008–1.291, P = .037) and heel regions (OR = 1.027, 95% CI: 1.000–1.054, P = .047). However, elevated plasma cortisol levels showed no significant causal association with OP in the FA (P = .939) or the whole body (P = .411) (Fig. 2A). Furthermore, no reverse causal relationship was found between elevated plasma cortisol levels and the occurrence or progression of OP. There was no evidence of heterogeneity (Cochran Q, P > .05) and no indication of pleiotropy in the MR-Egger analysis (MR-Egger intercept P > .05) (Fig. 2B; Fig. S1, Supplemental Digital Content, https://links.lww.com/MD/P784, Tables S1 and S2, Supplemental Digital Content, https://links.lww.com/MD/P785).

Figure 2.

Figure 2.

Open in a new tab

Assessing the causal relationship between cortisol levels, osteoporosis, and sarcopenia. (A) and (B) The causal relationship between cortisol levels and osteoporosis. (C) The causal relationship between cortisol levels and sarcopenia.

3.2. The causal effect of cortisol on SP

We subsequently examined the impact of cortisol on sarcopenia-related traits. The MR analysis revealed that the elevated plasma cortisol levels were significantly associated with reductions in WBLM (OR = 0.968, 95% CI: 0.951–0.986, P < .001), ALM (OR = 0.969, 95% CI: 0.944–0.996, P = .023), left grip hand strength (OR = 0.969, 95% CI: 0.949–0.990, P = .003) and right hand grip strength (OR = 0.977, 95% CI: 0.957–0.997, P = .030). There was no evidence of heterogeneity (Cochran Q P > .05) and no indication of pleiotropy in the MR-Egger analysis (MR-Egger intercept P > .05) (Fig. 2C; Fig. S2, Supplemental Digital Content, https://links.lww.com/MD/P784; Tables S1 and S3, Supplemental Digital Content, https://links.lww.com/MD/P785).

3.3. The causal effect of SP on OP

We extracted SNPs that were strongly associated with sarcopenia-related traits, defined as P < 5 × 10−8, and that were independent of one another, defined as a pairwise R2 < 0.01 based on the GWAS summary statistics. Next, we excluded the 3 independent SNPs previously identified as being associated with plasma cortisol concentrations and SP. Finally, according to result obtained from the causal relationship between cortisol and SP, we identified independent SNPs associated with SP and OP (LS-BMD, FN-BMD, H-BMD). Through MR analysis, we identified that the reduction in WBLM (OR = 1.103, CI: 1.013–1.202, P = .025), ALM (OR = 1.066, CI: 1.015–1.120, P = .011), left grip hand strength (OR = 1.423, CI: 1.170–1.732, P < .001) and right hand grip strength (OR = 1.378, CI: 1.155–1.646, P < .001) is significantly associated with a increased risk osteoporosis in LS. However, there is no causal relationship between sarcopenia-related traits and an increased risk of OP in the FN and heel (ALM-FN(P) = 0.598, WBLM-FN(P) = 0.610, left grip hand strength-FN(P) = 0.327, right hand grip strength-FN(P) = 0.929, ALM-H(P) = 0.000, WBLM-H(P) = 0.305, left grip hand strength-H(P) = 0.096, right hand grip strength-H(P) = 0.074). MR-Egger analysis detected no evidence of pleiotropy in the aforementioned results (ALM-LS(P) = 0.892, WBLM-LS(P) = 0.431, left grip hand strength-LS(P) = 0.151, right hand grip strength-LS(P) = 0.753, ALM-FN(P) = 0.122, WBLM-FN(P) = 0.480, left grip hand strength-FN(P) = 0.698, right hand grip strength-FN(P) = 0.752). But the heterogeneity test showed significant heterogeneity among selected IVs (P < .05) (Fig. 3, Tables S1, S4–S6, Supplemental Digital Content, https://links.lww.com/MD/P785).

Figure 3.

Figure 3.

Open in a new tab

Assessing the causal relationship between sarcopenia-related traits and osteoporosis.

3.4. Mediation analysis

The mediation effects of sarcopenia-related traits on the relationship between cortisol and lumbar OP are as follows: ALM was 0.064 (95% CI: −0.005–0.001, proportion: −1.5%), WBLM was 0.098 (95% CI: −0.012–0.005, proportion: −2.4%), left grip hand strength was 0.353 (95% CI: −0.080–0.058, proportion: −8.4%), right grip hand strength was 0.321 (95% CI: −0.064–0.049, proportion: −5.6%) (Table 2).

Table 2.

Associations between genetically estimated cortisol and osteoporosis outcomes after accounting for sarcopenia-related traits as mediators.

Mediator Total effect Direct effect Mediation effect (95% CI) Mediated proportion (%)
ALM 0.132 0.134 0.064 (−0.005, 0.001) −1.5
WBLM 0.132 0.135 0.098 (−0.012, 0.005) −2.4
Left-hand grip strength 0.132 0.143 0.353 (−0.080, 0.058) −8.4
Right-hand grip strength 0.132 0.139 0.321 (−0.064, 0.049) −5.6
Open in a new tab

The results of the mediation analysis and the proportion mediated are shown.

ALM = appendicular lean mass, WBLM = whole body lean mass.

4. Discussion

This MR study indicates a significant causal relationship between cortisol levels and the progression of OP in specific regions, including the FN and heel. Notably, elevated cortisol levels do not exhibit a potential causal associated with a reduction in T-BMD. It suggests that MCE may be primarily associated with the development and progression of localized OP. Moreover, it also indicate the substantial mediating role of sarcopenia between cortisol and lumbar OP.

The focus on OP is one of the initiatives that science and societies look for to address the social and health problems exacerbated by the global population aging. It is well known that OP, the most prevalent bone metabolic disorder regulated by multiple hormones, significantly impacts global public health by increasing fracture risks, particularly among post-menopausal women and the elderly.[38] Hormones play crucial roles in the development and progression of OP.[39] Understanding the relationship between each hormone and OP is essential for the prevention and treatment of OP. However, no randomized controlled trials have yet been conducted to investigate the relationship between a specific hormone and OP while fully controlling for the potential confounding effects of other hormones. We selected SNPs with specific effects on the SERPINA6/SERPINA1 locus, which influences CBG, as IVs. CBG, the primary binding protein for glucocorticoids, transports cortisol to target tissues, thereby regulating the availability of biologically active free cortisol in those tissues. Furthermore, these SNPs influence the genetic variability of plasma total cortisol concentrations, which to some extent reflects the potential activity of cortisol itself.[40] Leveraging genetic variants as robust IVs and controlling for the influence of other hormones, our study offers novel insights, suggesting that MCE is a significant potential factor in promoting OP progression in the FN. In contrast, MCE may help to prevent the onset and progression of OP in the LS and heel. This observation seems to be at variance with previous evidence and established conclusions. This may be attributed to MCE primarily impairing trabecular bone architecture and reducing overall bone quality, rather than directly affecting BMD.[7]

More importantly, our study demonstrated the mediating role of sarcopenia-related traits and quantified the proportions mediated by SP between MCE and lumbar OP. The research conducted by Mazziotti et al demonstrated that the risk of fractures is significantly elevated in patients with CD, with vertebral bodies being the most frequently affected skeletal sites.[8] Based on this study, we can reasonably speculate that this may be due to the LS’s increased susceptibility to the effects of SP. A study indicated that SP is a potential risk factor for acute osteoporotic vertebral fractures. This also indirectly supports the conclusions drawn from this study.[41] This reflects the importance of enhancing muscle training in the prevention of osteoporosis. It provides guidance for preventing osteoporosis in our daily lives through a healthy lifestyle.

A major strength of our study is that we applied instrumental variable analyses using genetic instruments that allowed for assessment of the causal role of SP mediators in the association between Cortisol and OP. Due to the random allocation of alleles and because these alleles are static throughout an individual’s life, this design is much less likely to be affected by nondifferential measurement error of the mediator and confounding compared with traditional observational studies.[42,43] This was further supported by our sensitivity analyses that did not indicate any bias due to pleiotropy.

This study also has some limitations. First, this study was based on morning plasma cortisol concentrations by immunoassay. Secondly, some of the individuals included in the GWAS of cortisol, grip strength, and lean mass were nonelderly; thus, this may be a potential sampling bias. The study focused primarily on individuals of European ancestry, which may limit generalization to other populations, highlighting the need for genomic studies in diverse ancestral groups. The MR framework relies on a key assumption that the risk conferred by an exposure is equivalent whether mediated by genetics or environment, and that genetic risk is conferred through the exposure of interest rather than via pleiotropic effects. Moreover, in the 2-sample MR analysis of SP and T-BMD, the inclusion of populations with different ancestries may introduce bias into our final results. Finally, we selected 3 SNPs as IVs for cortisol based on r2 < 0.3, among which there may be a small degree of linkage disequilibrium. When SNPs in linkage disequilibrium are used as independent IVs, the genetic effects on cortisol may be overestimated. Thus, rigorous clinical and fundamental research is needed in the future to provide more robust evidence for our conclusions.

5. Conclusion

This MR study suggests that MCE may be involved in the development of region-specific OP, particularly affecting the FN. Notably, MCE showed no significant association with generalized osteoporosis. SP mediates the development of osteoporosis in the spine. Therefore, our research further elucidates the underlying mechanisms by which MCE affects OP and highlights the importance of strengthening muscle training in the prevention of osteoporosis.

Author contributions

Methodology: Shijie Zhou.

Software: Mingqi Jiang.

Writing – original draft: Junyu Zhu, Jun Yan.

Writing – review & editing: Wenying Wang, Jun Yan.

Supplementary Material

Abbreviations:

ALM
appendicular lean mass
BMD
bone mineral density
FA
Forearm
FN
femoral neck
GWAS
genome-wide association study
H
heel
IV
instrumental variables
IVW
inverse-variance weighting
LS
lumbar spine
MCE
mild cortisol excess
MR
Mendelian randomization
OP
osteoporosis
SNPs
single-nucleotide polymorphisms
SP
sarcopenia
T-BMD
total-bone mineral density
WBLM
whole body lean mass.

All studies had been approved by a relevant ethical review board and participants had given informed consent, which did not require institutional review board approval because of the public characteristics of the data of GWAS.

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available for this article.

How to cite this article: Zhu J, Zhou S, Jiang M, Wang W, Yan J. Impact of mild cortisol excess on osteoporosis and the mediating role of sarcopenia-related traits: A Mendelian randomization study. Medicine 2025;104:35(e44153).

References

  • [1].Melis S, Trompet D, Chagin AS, Maes C. Skeletal stem and progenitor cells in bone physiology, ageing and disease. Nat Rev Endocrinol. 2025;21:135–53. [DOI] [PubMed] [Google Scholar]
  • [2].Mazziotti G, Angeli A, Bilezikian JP, Canalis E, Giustina A. Glucocorticoid-induced osteoporosis: an update. Trends Endocrinol Metab. 2006;17:144–9. [DOI] [PubMed] [Google Scholar]
  • [3].Sun H, Wu C, Hu B, Xie G. High prevalence of vertebral fractures associated with preoperative cortisol levels in patients with recent diagnosis of Cushing disease. Ann Med. 2023;55:2282183. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [4].Armas LA, Recker RR. Pathophysiology of osteoporosis: new mechanistic insights. Endocrinol Metab Clin North Am. 2012;41:475–86. [DOI] [PubMed] [Google Scholar]
  • [5].Willers C, Norton N, Harvey NC, et al. ; SCOPE review panel of the IOF. Osteoporosis in Europe: a compendium of country-specific reports. Arch Osteoporos. 2022;17:23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [6].Weiss MB, Syed SA, Whiteson HZ, Hirani R, Etienne M, Tiwari RK. Navigating post-traumatic osteoporosis: a comprehensive review of epidemiology, pathophysiology, diagnosis, treatment, and future directions. Life (Basel). 2024;14:561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [7].Pal R, Banerjee M, Prasad TN, et al. Fracture risk and bone health in adrenal adenomas with mild autonomous cortisol secretion/subclinical hypercortisolism: a systematic review, meta-analysis and meta-regression. J Bone Miner Res. 2024;39:885–97. [DOI] [PubMed] [Google Scholar]
  • [8].Mazziotti G, Frara S, Giustina A. Pituitary diseases and bone. Endocr Rev. 2018;39:440–88. [DOI] [PubMed] [Google Scholar]
  • [9].Trementino L, Appolloni G, Ceccoli L, et al. Bone complications in patients with Cushing’s syndrome: looking for clinical, biochemical, and genetic determinants. Osteoporos Int. 2014;25:913–21. [DOI] [PubMed] [Google Scholar]
  • [10].Li S, Luo X, Lu Z, Chen N. Association of midnight cortisol level with bone mineral density in Chinese patients with type 2 diabetes mellitus: a cross-sectional study. Diabetes Metab Syndr Obes. 2024;17:2943–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Yiallouris A, Tsioutis C, Agapidaki E, et al. Adrenal aging and its implications on stress responsiveness in humans. Front Endocrinol (Lausanne). 2019;10:54. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Gadelha M, Gatto F, Wildemberg LE, Fleseriu M. Cushing’s syndrome. Lancet. 2023;402:2237–52. [DOI] [PubMed] [Google Scholar]
  • [13].Garcia-Alfaro P, Garcia S, Rodriguez I, Pascual MA, Pérez-López FR. Association of endogenous hormones and bone mineral density in postmenopausal women. J Midlife Health. 2023;14:196–204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Xiao PL, Cui AY, Hsu CJ, et al. Global, regional prevalence, and risk factors of osteoporosis according to the World Health Organization diagnostic criteria: a systematic review and meta-analysis. Osteoporos Int. 2022;33:2137–53. [DOI] [PubMed] [Google Scholar]
  • [15].Vinolas H, Grouthier V, Mehsen-Cetre N, et al. Assessment of vertebral microarchitecture in overt and mild Cushing’s syndrome using trabecular bone score. Clin Endocrinol (Oxf). 2018;89:148–54. [DOI] [PubMed] [Google Scholar]
  • [16].Peeters GM, van Schoor NM, van Rossum EF, Visser M, Lips P. The relationship between cortisol, muscle mass and muscle strength in older persons and the role of genetic variations in the glucocorticoid receptor. Clin Endocrinol (Oxf). 2008;69:673–82. [DOI] [PubMed] [Google Scholar]
  • [17].Gonzalez Rodriguez E, Marques-Vidal P, Aubry-Rozier B, et al. Diurnal salivary cortisol in sarcopenic postmenopausal women: the osteolaus cohort. Calcif Tissue Int. 2021;109:499–509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [18].Guerri S, Mercatelli D, Aparisi Gomez MP, et al. Quantitative imaging techniques for the assessment of osteoporosis and sarcopenia. Quant Imaging Med Surg. 2018;8:60–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].He H, Liu Y, Tian Q, Papasian CJ, Hu T, Deng H-W. Relationship of sarcopenia and body composition with osteoporosis. Osteoporos Int. 2016;27:473–82. [DOI] [PubMed] [Google Scholar]
  • [20].Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Zheng HF, Forgetta V, Hsu YH, et al. ; AOGC Consortium. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature. 2015;526:112–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Medina-Gomez C, Kemp JP, Trajanoska K, et al. Life-course genome-wide association study meta-analysis of total body BMD and assessment of age-specific effects. Am J Hum Genet. 2018;102:88–102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Pei YF, Liu YZ, Yang XL, et al. The genetic architecture of appendicular lean mass characterized by association analysis in the UK Biobank study. Commun Biol. 2020;3:608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].Hinterwimmer F, Guenther M, Consalvo S, et al. Impact of metadata in multimodal classification of bone tumours. BMC Musculoskelet Disord. 2024;25:822. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med. 2007;4:e296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].Bolton JL, Hayward C, Direk N, et al. ; CORtisol NETwork (CORNET) Consortium. Genome wide association identifies common variants at the SERPINA6/SERPINA1 locus influencing plasma cortisol and corticosteroid binding globulin. PLoS Genet. 2014;10:e1004474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Crawford AA, Bankier S, Altmaier E, et al. ; CORtisol NETwork (CORNET) Consortium. Variation in the SERPINA6/SERPINA1 locus alters morning plasma cortisol, hepatic corticosteroid binding globulin expression, gene expression in peripheral tissues, and risk of cardiovascular disease. J Hum Genet. 2021;66:625–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Katsuhara S, Yokomoto-Umakoshi M, Umakoshi H, et al. Impact of cortisol on reduction in muscle strength and mass: a Mendelian randomization study. J Clin Endocrinol Metab. 2022;107:e1477–87. [DOI] [PubMed] [Google Scholar]
  • [30].Morris JA, Kemp JP, Youlten SE, et al. ; 23andMe Research Team. An atlas of genetic influences on osteoporosis in humans and mice. Nat Genet. 2019;51:258–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology. 2017;28:30–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33].Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Bowden J, Del Greco M F, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol. 2016;45:1961–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Burgess S, Thompson SG. Bias in causal estimates from Mendelian randomization studies with weak instruments. Stat Med. 2011;30:1312–23. [DOI] [PubMed] [Google Scholar]
  • [37].Rogne T, Gill D, Liew Z, et al. Mediating factors in the association of maternal educational level with pregnancy outcomes: a Mendelian randomization study. JAMA Netw Open. 2024;7:e2351166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38].Onizuka N, Onizuka T. Disparities in osteoporosis prevention and care: understanding gender, racial, and ethnic dynamics. Curr Rev Musculoskelet Med. 2024;17:365–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [39].Umur E, Bulut SB, Yigit P, et al. Exploring the role of hormones and cytokines in osteoporosis development. Biomedicines. 2024;12:1830. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [40].Meyer EJ, Nenke MA, Rankin W, Lewis JG, Torpy DJ. Corticosteroid-binding globulin: a review of basic and clinical advances. Horm Metab Res. 2016;48:359–71. [DOI] [PubMed] [Google Scholar]
  • [41].Hida T, Shimokata H, Sakai Y, et al. Sarcopenia and sarcopenic leg as potential risk factors for acute osteoporotic vertebral fracture among older women. Eur Spine J. 2016;25:3424–31. [DOI] [PubMed] [Google Scholar]
  • [42].Sanderson E, Glymour MM, Holmes MV, et al. Mendelian randomization. Nat Rev Methods Primers. 2022;2:6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [43].Blakely T, McKenzie S, Carter K. Misclassification of the mediator matters when estimating indirect effects. J Epidemiol Community Health. 2013;67:458–66. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

medi-104-e44153-s001.docx (4MB, docx)
medi-104-e44153-s002.docx (20KB, docx)

Articles from Medicine are provided here courtesy of Wolters Kluwer Health

RESOURCES