Abstract
Objective
The aim was to investigate the relationship between metabolic indices and abnormal bone mass (ABM), analyse the association between osteoprotegerin (OPG) gene mutations and ABM, and explore the interaction effect of type 2 diabetes mellitus (T2DM) and OPG gene mutations on bone mineral density (BMD) in postmenopausal women to provide a new supplementary index and a reliable basis for the early identification of osteoporosis (OP) in postmenopausal women in the clinical setting.
Methods
Postmenopausal women hospitalized within the Department of Endocrinology of the First Affiliated Sanatorium of Shihezi University from June 2021 to March 2023 were retrospectively analysed, and the bone mineral density of lumbar vertebrae 1–4 (BMD (L1-4)) of the studied subjects was measured once via twin-energy X-ray absorptiometry. The studied subjects were divided into a normal bone mass (NBM) group and an ABM group according to their bone mineral density, and the general data of the studied subjects were recorded once. Blood biochemical indices were determined, and genotyping of the rs4355801 locus of the OPG gene was performed. Differences in the overall data and biochemical indices of the two groups were evaluated via the rank-sum test, and the relationship between blood glucose levels and mutations of the rs4355801 locus of the OPG gene and ABM or BMD (L1-4) was evaluated via binary logistic regression analysis or linear regression analysis. A bootstrap test was performed to test whether uric acid (UA) levels mediate the association between blood glucose levels and BMD (L1-4). Simple effect analysis was performed to analyse the interaction between T2DM and mutations at the rs4355801 locus of the OPG gene on BMD (L1-4).
Results
① After adjusting for confounding factors, the risk of ABM increased by 50% (95% CI 21–85%) for each unit increase in fasting plasma glucose (FPG) levels and 31% (95% CI 2–69%) for each unit increase in glycosylated haemoglobin (HbA1c) levels (both P < 0.05). FPG levels were negatively correlated with BMD (L1-4) (both P < 0.05), and uric acid in blood sugar and BMD (L1-4) played a significant mediating role in the model; this mediation accounted for 21% of the variance. ② After adjusting for confounding factors, women with the mutant genotypes GA and GG + GA of the OPG gene rs4355801 locus had a lower risk of ABM than did those with the wild-type genotype AA (OR = 0.71, 95% CI = 0.52–1.00; OR = 0.51, 95% CI = 0.28–0.92, P < 0.05). The mutant genotypes GG, GA and GG + GA were positively correlated with BMD (L1–4) (all P < 0.05). The interaction between T2DM and mutations in the OPG gene rs4355801 locus had an effect on BMD (L1-4), and this site mutation weakened the increase in blood glucose levels and led to an increase in the risk of ABM (P < 0.05).
Conclusion
Elevated blood glucose levels in postmenopausal women were associated with an increased risk of ABM, and UA played a mediating role in the relationship FPG levels and BMD (L1-4), accounting for 21% of the variance. Mutations at the rs4355801 locus of the OPG gene were associated with a reduced risk of ABM in postmenopausal women. The interaction between T2DM and mutations at the rs4355801 locus of the OPG gene in postmenopausal women affects BMD (L1-4), and mutations at this locus attenuate the increased risk of ABM due to elevated blood glucose levels.
Keywords: Blood glucose, Uric acid, OPG gene, Abnormal bone mass
Introduction
As women age, their nutritional needs change [1], and postmenopausal women, in particular, experience changes in body composition, including increased in adiposity and decreases in bone mineral density (BMD) [2]. Studies have shown that changes in metabolic function and body composition, including the development of type 2 diabetes (T2DM), osteoporosis (OP), and cardiovascular disease, increase the risk of chronic diseases in postmenopausal women compared with younger/premenopausal women [3]. Among postmenopausal women, T2DM is a systemic metabolic disorder characterized mainly by hyperglycaemia [4]. T2DM not only affects the metabolism of the three major nutrients in the body but is also closely related to bone metabolism, especially osteoporosis [5]. OP is a type of systemic bone pathology, and the risk of developing osteoporosis is greater in postmenopausal women and elderly individuals [6]. ABM includes bone loss and osteoporosis, and the provision of timely treatment to patients with bone loss can delay the onset of OP [7]. Osteoprotegerin (OPG) is a glycoprotein involved in the regulation of bone remodelling, and OPG regulates osteoclast activity by blocking the interaction between receptor activator of NF-κB (RANK) and receptor activator of NF-κB ligand (RANKL) [8]. Studies have shown that the OPG gene is closely related to OP. Guo [9] studied the effects of mutations at the rs3102735 and rs20736168 loci of the OPG gene in postmenopausal women and reported that these gene polymorphisms can be used along with other genetic markers to identify individuals at high risk of osteoporosis. Other studies from China, Europe, the United States and India have shown [10–13] that genetic polymorphisms at the rs6469804, rs4355801, rs6469792, rs1032129, rs2073618 and rs6993813 loci of the OPG gene are significantly associated with BMD and OP. Numerous OPG gene loci have been studied; however, the rs4355801 locus is most explored to date, and conclusions are inconsistent. In summary, OP is closely related to T2DM and the OPG gene, but few studies have explored the interactions among T2DM, OPG gene mutations and OP. To provide a reliable basis for the early identification and prevention of OP, we expanded the scope of bone metabolism research to ABM; therefore, the present study aimed to investigate the factors influencing ABM in postmenopausal women, analyse the association between OPG gene mutations and ABM, and investigate the effects of T2DM and OPG gene mutations on BMD in postmenopausal women to provide a new additional indicator and a reliable basis for the early identification of OP in postmenopausal women in clinical practice.
Materials and methods
Research subjects
From June 2021 to March 2023, an endocrinologist at the first Affiliated Sanatorium of Xinjiang Shihezi University endocrinologist selected 200 outpatient and inpatient postmenopausal women for this study according to their records. The diagnostic criteria for ABM were based on the 2017 guidelines for the diagnosis and treatment of primary osteoporosis [14], and the diagnostic criteria for T2DM were based on the Chinese guidelines for the prevention and treatment of type 2 diabetes mellitus (2020 version) [15].
The exclusion criteria were as follows: (1) patients with type 1 diabetes and other endocrine or autoimmune diseases; and (2) patients with serious liver or kidney disease, other congenital osteoporosis, the use of drugs that affect bone metabolism, malignant tumours or mental disorders.
Methods
Collection of baseline data
The participants’ age, waist circumference, hip circumference, height, weight, waist‒hip ratio (WHR), and body mass index (BMI) were recorded. The next morning, after fasting, 5 mL of venous blood was extracted overnight. With the help of an automatic biochemical analyser (Roche, Cobas8000 c701), fasting plasma glucose (FPG), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), calcium (Ca), phosphorus (P), alkaline phosphatase (ALP), uric acid (UA), and creatinine (Cre) levels and other blood biochemical indices were detected. Glycosylated haemoglobin (HbA1c) was detected via high-pressure liquid chromatography (HPLC).
Measurement of BMD
The bone mineral density (BMD) of the lumbar region of the spine (L1-4) was measured via dual-energy X-ray absorptiometry (Prodigy). Bone abnormalities included bone loss (2.5 SD < T value 1.0 SD or less) and OP (T value 2.5 SD or less).
Gene distribution
A DNA extraction kit (purchased from QIAGEN) was used to extract DNA, which was stored at − 70 °C. Time-of-flight mass spectrometry was used to determine the genotype of the OPG gene rs4355801 locus.
The examination was permitted by the Ethics Committee of the First Affiliated Sanatorium of Shihezi University, and all participants signed an informed consent form. This study strictly followed the Declaration of Helsinki.
Statistical analysis
SPSS 25.0 statistical software was used to measure data that did not follow a normal distribution, so the medians (P25 ~ P75) are reported; the Wilcoxon rank test was used to compare the NBM group with the ABM group with respect to each variable, and the difference was statistically significant. The χ2 test was used to evaluate whether the OPG gene rs4355801 locus met the Hardy‒Weinberg genetic laws of balance; the binary logistic regression model was used to investigate the sugar metabolism index and rs4355801 OPG gene mutations associated with ABM, and the OR and 95% confidence interval (95% confidence interval (CI)) represent the strength of the relationship. A linear regression model was used to explore the sugar metabolism index and rs4355801 OPG gene mutations associated with BMD (L1-4), and the standardized regression coefficient βand 95% CI are used for the strength of the association. Via SPSS software with the PROCESS plug-in, after bootstrapping 5000 times, we checked whether UA levels mediated the relationship between FPG levels and BMD (L1-4). Simple effect analysis of the effects of T2DM and the rs4355801 locus mutation on BMD (L1-4) was performed. As shown in Fig. 1, P < 0.05 was considered to be statistically significant.
Fig. 1.
Conceptual diagram of the mediating role of uric acid levels in the relationship between fasting blood glucose levels and BMD (L1-4)
This study adopted three models:
Model 1: not adjusted;
Model 2: adjusted for age, BMI, and waist circumference;
Model 3: On the basis of Model 2, Model 3 was further adjusted for TG, HDL-C, LDL-C, UA, and Cre levels.
Results
Basic characteristics of the study subjects
Compared with the NBM group, the ABM group was significantly older and had higher FPG and HbA1c levels and significantly lower waist circumference, UA, and BMD (L1-4) values (P < 0.05) (Table 1).
Table 1.
Basic characteristics of the study population
| Variables of interest | NBM group | ABM group | Z | P value |
|---|---|---|---|---|
| Age (years) | 59 (48.75–66) | 68 (58–73) | − 4.17 | < 0.001 |
| BMI (kg/m2) | 26.07 (23.76–27.58) | 25.78 (23.44–28.41) | − 0.34 | 0.74 |
| Waist circumference (cm) | 91 (85–100) | 87 (81–96) | − 2.73 | 0.01 |
| WHR | 0.91 (0.85–0.96) | 0.93 (0.87–0.97) | − 1.27 | 0.21 |
| FPG | 5.02 (4.57–6.61) | 8.17 (6.98–10.06) | − 5.07 | 0.01 |
| HbA1c | 5.96 (5.66–6.45) | 8.00 (7.10–9.15) | − 5.09 | 0.02 |
| TG | 1.22 (0.91–1.75) | 1.42 (1.00–1.97) | − 0.57 | 0.57 |
| HDL-C | 1.32 (1.04–1.56) | 1.39 (1.15–1.93) | − 0.68 | 0.50 |
| LDL-C | 2.51 (1.90–3.05) | 2.33 (1.48–3.34) | − 1.10 | 0.27 |
| Ca | 2.27 (2.21–2.33) | 2.29 (2.19–2.39) | − 0.19 | 0.85 |
| P | 1.14 (1.03–1.28) | 1.12 (1.01–1.22) | − 1.73 | 0.08 |
| ALP | 74.00 (60.00–90.00) | 71.00 (59.50–82.00) | − 1.65 | 0.09 |
| UA | 299.00 (262.00–327.00) | 287.00 (236.00–313.50) | − 6.94 | 0.03 |
| Cre | 57.20 (50.00–66.20) | 59.90 (50.40–66.60) | − 0.17 | 0.86 |
| BMD (L1-4) | 1.02 (0.89–1.21) | 0.96 (0.88–1.11) | − 11.24 | < 0.001 |
Associations of glucose metabolism indices with abnormal bone mass and bone metabolism indices
With ABM or BMD (L1-4) as the dependent variable, different variables were included, and different models were established to analyse the associations between glucose metabolism indices and ABM or BMD (L1-4). The results showed that increased blood sugar levels were associated with an increased risk of ABM. After all the confounding factors were adjusted for, an increase of 1 unit in FPG led to a 50% increased risk of ABM (95% CI 21 ~ 85%), and an increase of 1 unit in HbA1c led to a 31% increased risk of ABM (95% CI 2 ~ 69%) (P < 0.05). FPG was negatively correlated with BMD (L1-4), and the difference was statistically significant (P < 0.05) (Tables 2, 3).
Table 2.
Association between glucose metabolism indices and bone mass abnormalities
| Outcome | Measures of glucose metabolism | logistic regression ORs(95% CI) | ||
|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | ||
| ABM | FPG | 1.34 (1.15–1.55) ** | 1.38 (1.17–1.63) ** | 1.50 (1.21–1.85) ** |
| HbA1c | 1.36 (1.11–1.67) ** | 1.29 (1.04–1.59)* | 1.31 (1.02–1.69)* | |
* P < 0.05; **P < 0.01
Table 3.
Association between the levels of glucose metabolism indicators and bone metabolism indicators
| Outcome | Measures of glucose metabolism | Linear regression β(95% CI) coefficient | ||
|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | ||
| ABM | FPG | − 0.03 (− 0.04–0.01)** | − 0.02 (− 0.03–− 0.01)* | − 0.02 (− 0.03–− 0.01)* |
| HbA1c | − 0.005 (− 0.02–0.01) | − 0.001 (− 0.02–0.02) | 0.005 (− 0.01–0.02) | |
* P < 0.05; **P < 0.01
Mediating role of uric acid in the relationship between blood glucose and lumbar spine (L1-4) bone mineral density
Analysis of the mediating effect of UA levels on the association between FPG levels and BMD (L1-4) was performed. As shown in the results, an increase in blood sugar had a direct effect of 1.353 on the risk of abnormal bone mass (95% CI 1.199–1.539); the indirect effect of UA levels was 0.01, and the 95% confidence interval [0.01, 0.001] did not contain 0; thus, UA levels play a significant mediating role in the association, and the mediation ratio was 21% (Tables 4, 5).
Table 4.
Associations among uric acid, blood glucose, and bone metabolism indicators
| The results of the variable | Predictor variable | Fitting index | Significant coefficient | ||
|---|---|---|---|---|---|
| R2 value | F value | t value | βvalue | ||
| UA | FPG | 0.03 | 6.53 | − 2.56 | − 4.18 |
| BMD (L1-4) | FPG | 0.11 | 23.79 | − 4.88 | − 0.03 |
| BMD (L1-4) | FPG | 0.25 | 33.36 | − 4.13 | − 0.02 |
| UA | 6.2 | 0 | |||
Table 5.
Bootstrap mediation affects test results
| Effects of relationship | Effect of value | LLCI | ULCI | Effect |
|---|---|---|---|---|
| Total effect | − 0.03 | − 0.04 | − 0.02 | |
| Direct effect | − 0.02 | − 0.03 | − 0.01 | 79% |
| Indirect effect | − 0.01 | − 0.01 | − 0.001 | 21% |
Association of OPG gene rs4355801 gene mutation with abnormal bone mass and bone metabolism indices
The associations between the OPG gene rs4355801 genotype and ABM disease and BMD (L1-4) were analysed with different models. The mutant GA genotype was negatively associated with the risk of ABM in all models after adjusting for all confounding factors (OR = 0.71, 95% CI = 0.52–1.00). Moreover, the mutation GG + GA genotype in Models 1 and 2 was negatively correlated with the risk of ABM after some confounding factors were adjusted for (OR = 0.51, 95% CI = 0.28–0.92) (both P < 0.05). In all the models, compared with the wild-type AA + GA mutation genotype, the GG, GA, and GG genotypes were related to BMD (L1-4), and the differences were statistically significant (P < 0.05) (Tables 6 and 7).
Table 6.
Association of mutations at the rs4355801 locus of the OPG gene with abnormal bone mass
| Outcome | SNP genotype | Example (human) | Logistic regression ORs (95% CI) | ||
|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | |||
| ABM | AA | 84 | 1 | 1 | 1 |
| GG | 24 | 0.59(0.23–1.53) | 0.60 (0.23–1.56) | 0.60 (0.22–1.64) | |
| GA | 92 | 0.69 (0.51–0.93)* | 0.70 (0.51–0.95)* | 0.71 (0.52–1.00)* | |
| GG + GA | 116 | 0.49 (0.28–0.88)* | 0.51 (0.28–0.92)* | 0.545 (0.30–1.00) | |
* P < 0.05
Table 7.
Association between mutations at the rs4355801 locus of the OPG gene and bone metabolism indices
| Outcome | SNP Genotype | Cases (persons) | Linear regression beta coefficient (95% CI) | ||
|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | |||
| ABM | AA | 84 | 0 | 0 | 0 |
| GG | 24 | 0.22 (0.17–0.32)** | 0.23(0.16–0.30)** | 0.19(0.13–0.25)** | |
| GA | 92 | 0.04 (0.01–0.07)** | 0.04 (0.01–0.06)* | 0.03 (0.01–0.06)* | |
| GG + GA | 116 | 0.12 (0.06–0.17)** | 0.10 (0.05–0.16)** | 0.09(0.04–0.13)** | |
* P < 0.05;**P < 0.01
Association of T2DM with mutations at the rs4355801 locus of the OPG gene and the effect on BMD (L1-4)
The table shows that T2DM and rs4355801 locus gene mutations affect BMD (L1-4) and that the locus mutation weakens the risk of high blood sugar, leading to an increased risk of ABM (P < 0. 05) (Tables 8, 9).
Table 8.
Between-subjects effect analysis of the interaction association between T2DM and mutations at the rs4355801 locus of the OPG gene and the effect on BMD (L1-4)
| Influencing Factors | BMD (L1-4) | |
|---|---|---|
| F value | P value | |
| Rs4355801 & T2DM | 9.78 | 0.001 |
Table 9.
Pairwise comparison of the association of T2DM with mutations at the rs4355801 locus of the OPG gene and the effect on BMD (L1-4)
| Genetic mutations | T2DM | BMD (L1-4) | ||
|---|---|---|---|---|
| Mean difference | P value | Difference (95% CI) | ||
| There are none | No-have | 0.081 | 0.05 | 0.001–0.161 |
| There are | None—Have | 0.107 | 0.007 | 0.03–0.184 |
Discussion
Osteoporosis is the most unusual systemic bone ailment worldwide. Postmenopausal women with T2DM have a significantly increased risk of fracture [16–19], which significantly influences their quality of life. The results revealed that the risk of ABM increased with increasing blood glucose in postmenopausal women and that FPG was negatively correlated with BMD (L1-4), which was consistent with the findings of Ali B. Roomi’s study of postmenopausal women [20] [21]. However, in Zhong Jiao [22] study [22], the analysis revealed that BMD increased with increasing BMI and waist circumference in the elderly diabetic population in China, indicating that the positive effect of diabetes on bone quality was due mainly to the positive effect of obesity. This is different from the results of our work, which may be related to the one-of-a-kind population and sample size. Moreover, among the many metabolic indicators, a positive correlation between serum UA levels and BMD has been reported in postmenopausal women with T2DM [23] [24]. The results of the analysis of the mediating effects in this study suggest that uric acid, blood sugar and BMD (L1-4) in postmenopausal osteoporosis patients play partial mediating roles in 21% of the interfaces. An increase in blood glucose may not only indirectly affect the risk of ABM by affecting uric acid levels but also directly affect the risk of ABM. However, the results of studies on uric acid as a protective factor against osteoporosis risk factors remain controversial [25]. Therefore, more studies are needed in the future to determine the exact mechanism and effect of uric acid on osteoporosis.
OPG acts as a RANKL decoy receptor, and this combination prevents the interaction of NF-κB with RANKL, thus inhibiting osteoclast formation. Therefore, OPG is considered to be a potent inhibitor of osteoclast bone resorption. Moreover, low levels of OPG cause osteoporosis [26], and OPG levels are significantly increased in postmenopausal OP patients after treatment [27]. A large variety of studies have shown that OPG gene mutations are associated with the risk of OP [9] [13]. In this study population, logistic regression analysis revealed that in the rs4355801 OPG gene locus, compared with the wild-type AA genotype, GG + GA GA genotypes were negatively correlated with the risk of ABM. Linear regression analysis revealed that the mutant genotypes of the OPG gene rs4355801 locus were positively correlated with BMD (L1-4), indicating that the wild-type AA genotype might be a risk factor for ABM. This finding is consistent with the results of a previous study [28] that showed that the wild-type AA genotype of the OPG gene rs4355801 locus is associated with decreased BMD of the lumbar spine (L1‒4) and femoral neck. Patients with the A allele have significantly decreased BMD and an increased risk of OP.
OPG–RANKL–RANK shafts were initially involved in bone remodelling and osteoporosis and are now considered potential contributors to the pathogenesis of T2DM, and OPG is considered a potential factor regulating glucose metabolism [29]. In the clinic, type 2 diabetes has been associated with elevated serum OPG concentrations [30]. Research has revealed that the interaction between T2DM and OPG gene rs4355801 locus mutations have multiple effects on BMD (L1-4), weakens the association of hyperglycaemia with an increased risk of ABM, and strengthens the association of the gene mutation with a greater BMD, which is consistent with the results of a previous study [31] on the association between OPG gene mutations and fracture risk in the elderly population.
The limitations of this study are that the study should have included more OPG loci, and more studies are needed to confirm our findings, especially in larger samples and in different ethnic groups. The mechanisms affecting bone mass abnormalities are complex, and our study will expand the research on the aetiology of OP and provide a new basis for gene-targeted therapy for OP.
Conclusion
In conclusion, uric acid and the OPG gene rs4355801 mutation play important roles in the association between T2DM and ABM. Therefore, in postmenopausal women with T2DM, this new supplementary index can be used for the diagnosis of ABM, and it can provide a reliable basis for the early identification and timely intervention of OP in postmenopausal women.
Author contributions
Jun Li is the corresponding author and first author, and Zixin Li and Siyuan Li are the co-first authors. Jun Li, Zixin Li and Siyuan Li wrote the main text and figures of the manuscript. All authors were involved in data collation and reviewed the manuscript.
Funding
This work was supported through funding from the Corps Guidance Science and Technology Programme: The Role and Mechanism of FOSL2 in the Development of Type 2 Diabetes (2022ZD044); the International Science and Technology Cooperation Promotion Programme: Role and Mechanism of FOSL2 in T2DM through the TGF-β1/Smad3 Signalling Pathway (GJHZ202206); and the Science and Technology Tackling Programme in Key Areas: Demonstration and Application of the MDT Standardised Diagnosis and Treatment Programme for Endocrine Hypertension in the Southern Border Region (2021AB031).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
No datasets were generated or analysed during the current study.