New research by Queen Mary University of London and the University of Southampton’s Medical Research Council Lifecourse Epidemiology Unit (MRC LEU) has found associations between lower bone mineral density and worse cardiovascular health in both men and women.
Published in the Journal of Bone and Mineral Research, the study used the internationally unique UK Biobank cohort to investigate links between bone and cardiovascular health.
They used a combination of imaging and blood biomarker data to investigate the relationship in the largest sample of people reported to date.
Osteoporosis and heart disease are important public health problems.
These conditions share a number of risk factors such as increasing age, smoking, and a sedentary lifestyle. Research shows that there may be links between the two conditions even after accounting for shared risk factors.
This suggests that there may be biological pathways linking the two conditions, and investigating these links could reveal targets for novel drug therapies.
However, current research studies lack objective measures of bone and heart health and are often limited to studies of small numbers of people for relatively short periods of time.
The researchers found that lower bone density was linked to greater arterial stiffness (indicating poor cardiovascular health) in both men and women.
They also found that individuals with poor bone health had an increased risk of dying from ischaemic heart disease.
These links were not explained by shared risk factors or traditional cardiovascular risk factors. Interestingly, they found that the mechanisms underlying the bone-heart relationship appeared different in men and women.
Dr. Zahra Raisi-Estabragh, BHF Clinical Research Training Fellow from Queen Mary University of London, led the analysis. She said: “Our study demonstrates clear links between bone disease and cardiovascular health. The underlying pathophysiology of the bone heart axis is complex and multifaceted and likely varies in men and women.”
Professor Nick Harvey, Professor of Rheumatology and Clinical Epidemiology at the MRC LEU, University of Southampton, who supervised the work added: “The wealth of information available in the UK Biobank permitted a highly detailed analysis of the complex interactions between musculoskeletal and cardiovascular health, helping to elucidate potential underlying mechanism, and informing novel approaches to clinical risk assessment.”
Professor Steffen Petersen, Professor of Cardiology at Queen Mary University of London co-supervised the project. He comments: “Increasing our understanding of novel determinants of heart disease, such as the bone-heart axis, is key to improving disease prevention and treatment strategies and for improving population health.”
Professor Cyrus Cooper, Director of the MRC LEU, University of Southampton, added: “This study directly complements our program of research investigating the lifecourse determinants of musculoskeletal health and disease.
It illustrates the importance for the University of Southampton and the MRC LEU of our ongoing contribution to the leadership of the large, state-of-the-art, multidisciplinary Imaging Study as part of the unique world-leading UK Biobank resource.”
Cardiovascular disease and osteoporosis are two major factors that seriously affect the quality of life and mortality of middle-aged and elderly people. These diseases accompany the ageing process, affecting the length and quality of life of middle-aged and elderly individuals.
There is growing evidence that the coincidental occurrence of both diseases may be related to common pathological mechanisms other than age.1
Arterial stiffness is one of the basic pathologies of cardiovascular disease.2 The degree of arterial stiffness is often used as an important predictor for the diagnosis and prognosis of cardiovascular diseases.3 4
Among many methods for evaluating vascular lesions, carotid arterial ultrasound, overspeed CT and MRI can be used to evaluate the abnormal structure of arterial walls.3
However, when the arterial wall structure is abnormal, the degree of arterial stiffness is severe. Therefore, early evaluation of arterial stiffness before the structure becomes abnormal is very important for predicting the risk of cardiovascular events.
There are many clinical indicators to identify early arterial stiffness. Among them, pulse wave velocity (PWV) is often used to assess the early stage of arterial stiffness.5 However, blood pressure at the time of measurement can affect the PWV values.
In addition, the cardio-ankle vascular index (CAVI) is a relatively new non-invasive indicator of arterial stiffness, which is performed by integrating the ECG, phonocardiogram and arterial pulse waveform techniques. CAVI can reflect the overall arterial elasticity from the origin of the aorta to the ankle artery. CAVI=(2ρ/ΔP)×ln (Ps/Pd) ×PWV2 (ρ=blood density, ΔP=pulse pressure, Ps=systolic blood pressure, Pd=diastolic blood pressure).
The measurement of the CAVI is not affected by blood pressure, and CAVI could represent the stiffness of the arterial tree from the origin of the aorta to the ankle.6
Thus, CAVI is widely used in the evaluation of cardiovascular diseases and related risk factors for arterial stiffness. In previous studies, elevated CAVI values demonstrated a good predictive ability for cardiovascular diseases.6
Recently, several studies have suggested that there may be an association between arterial stiffness and bone mineral density (BMD).
However, these studies were mostly conducted in postmenopausal women, patients with chronic kidney disease, patients undergoing haemodialysis or individuals with hypertension.7–9
Few studies have been conducted in the general population, and few studies have included men. Moreover, the findings of these studies have been inconsistent.10
There is only one study about the association between BMD and arterial stiffness that measured CAVI. Masugata’s study showed that elevated CAVI values are associated with reduced BMD in patients with hypertension.8
The aim of this study was to investigate whether there is an association between arterial stiffness, measured by the CAVI, and BMD in inpatients aged 50 years and older in China. In addition, we studied the effects of age, gender, serum lipid levels and body mass index (BMI) on the association.
This was a retrospective and observational cross-sectional study that was conducted from 1 September 2015 to 31 May 2017 with 580 inpatients from the Department of Geriatrics at our hospital. Online Supplementary file S1 describes the participants’ enrolment and reasons for exclusion.
Inclusion criteria (all of the following were met): Asian; age ≥50 years; postmenopausal (if women); and ability to cooperate to provide a medical history, obtain an effective measurement of the CAVI and undergo a complete examination.
Exclusion criteria (excluding those who satisfied one or more of the following conditions): patients with incomplete data, limb disability, acute infection, acute myocardial infarction, acute cerebral infarction, renal failure dialysis, a malignant tumour in an active phase, hormone replacement therapy and oral or injected glucocorticoid use.
When the ankle brachial index (ABI) is less than 0.9, the lower CAVI value cannot reflect the actual degree of arterial stiffness,11 so we exclude individuals with an ABI value less than 0.9 on either side.
Measurement of BMD
Dual-energy X-ray absorptiometry (DEXA) scans (Prodigy, Lunar, Madison, Wisconsin, USA) were performed and analysed according to the manufacturer’s standard scanning and positioning protocols. The BMD of the lumbar spine (LS BMD), the bilateral femoral neck (FN BMD) and the total hip (TH BMD) were measured.
Measurement of CAVI and ABI
The CAVI and ABI were measured by the VaSera VS-1500 (Fukuda Denshi Co Ltd, Tokyo, Japan) blood pressure pulse measuring device. All the measurements were taken by the same experienced operator on the same machine using standardised procedures for participant positioning. Electrocardiograph electrodes were placed on the patient’ wrists.
A microphone was placed on the sternum for capturing heart sounds, and appropriate cuffs were wrapped to each of the patient’s arms and ankles. After the measurements were completed, software was used to analyse the obtained data and, thus, the CAVI and ABI values. In our study, we used the mean values of CAVI on the left and right sides.
The data required for this study were obtained from the medical records of the patients from their hospitalisation. General clinical data, such as age, gender, smoking history, history of diabetes mellitus (DM) and history of cardiovascular or cerebrovascular disease (CVD, including coronary heart disease, cerebral infarction/transient ischaemic attack), were obtained by reading the medical records. Height and weight were measured in conjunction with the CAVI measurements.
The participants did not smoke or drink coffee for at least half an hour before undergoing the blood pressure measurement. The patients had empty bladders and sat for at least 5 min before the measurement. Then, the blood pressure of the upper arm was measured with a mercury sphygmomanometer by the doctor. Blood pressure included systolic blood pressure (SBP) and diastolic blood pressure (DBP). The data used were the averages of two measurements taken in at least 5 min intervals.
White cell count (WCC), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum creatinine, cystatin C, uric acid (UA), fibrinogen and erythrocyte sedimentation rate (ESR) were obtained via a fasting venous blood test on the second day of admission. The neutrophil-to-lymphocyte ratio (NLR) is the ratio of the neutrophil count to the lymphocyte count. The estimated glomerular filtration rate (eGFR) was calculated using the chronic kidney disease epidemiology collaboration (CKD-EPI) creatinine-cystatin C formula.12 Online Supplementary file S2 shows the details of this formula.
Patient and public involvement
Patients were not involved in the design or conduct of the study. Study reports will be disseminated to investigators and patients through this open-access publication.
This study was conducted in accordance with the contents of the Declaration of Helsinki. Since this study was a retrospective anonymous study, informed consent from the patients was not required.
First, the distributions of the clinical characteristics were expressed as the mean and SD for continuous variables and the frequency and percentage for categorical variables according to the following age groups: 50–59 years old, 60–69 years old and 70 years and older. Then, continuous variables were tested by two independent samples t-tests, and the categorical variables were tested by the χ2 test.
Second, bivariate correlation analyses were conducted between the CAVI values and each of the possible variables (LS BMD, FN BMD, TH BMD, age, gender, smoking status, cerebrovascular disease (CVD), DM, BMI, SBP, DBP, ABI, WCC, NLR, fibrinogen, TG, TC, HDL-C, LDL-C, UA, ESR and EGFR). The correlation between CAVI and gender, smoking, the history of CVD and DM were analysed by Kendall’s tau-b correlation analysis with others by Pearson correlation analysis. Then, we drew scatter diagrams to visually show the correlation between the CAVI values and BMD of the different body areas.
Finally, a linear regression analysis was performed to estimate the strength of the correlation between the variables and the CAVI values, and the results are described as the unstandardised coefficients B (95% CI) and the p value.
Model 1 is the crude model without any adjustments; model 2 was adjusted for age, gender, BMI, smoking status, history of CVD and history of DM; model 3 adjusted for SBP, HDL-C, blood UA, fibrinogen and eGFR in addition to the adjustments from model 2. Analyses were performed with the SPSS statistical software package, V.19.0. P<0.05 (bilateral) was defined as statistically significant.
Table 1 shows the characteristics of the 580 patients included in this study, 366 (63.1%) of whom were male. The mean (SD) age of the participants was 64.82 years (SD=11.377 years). All patients were divided into three groups according to age: 50–57 years old, 58–68 years old and 69 years and older.
There were no significant differences in gender or BMI between the different age groups.
With increasing age, the prevalence of cardiovascular and cerebrovascular diseases and type 2 DM increased. SBP, CAVI values, NLR, fibrinogen and ESR also increased with age. However, the decreasing BMD values were found in FN and TH, as age increased. Table 2 compares characteristics between different genders, demonstrating no difference in age or CAVI between them.
Clinical characteristics of the different age groups
|Characteristic or parameter||50-57 years (n=196)||58–68 years (n=190)||69–94 years (n=194)||Total (n=580)||P value|
|Gender (male, %)||135 (68.9%)||114 (60.0%)||117 (60.3%)||366 (63.1%)||0.122|
|Smoking (%)||71 (36.2%)||56 (29.5%)||39 (20.1%)||166 (28.6%)||0.002|
|CVD (%)||45 (23.0%)||65 (34.2%)||123 (63.4%)||233 (40.2%)||<0.001|
|DM (%)||82 (41.8%)||91 (47.9%)||112 (57.7%)||285 (49.1%)||0.006|
|SBP, mm Hg||126.88±15.47||129.54±16.25||139.51±18.66||131.97±17.68||<0.001|
|DBP, mm Hg||83.19±10.77||81.36±9.91||80.13±10.7||81.57±10.53||0.015|
|LS BMD, g/cm2||1.13±0.17||1.08±0.19||1.09±0.23||1.10±0.20||0.068|
|FN BMD, g/cm2||0.91±0.13||0.87±0.14||0.79±0.15||0.86±0.15||<0.001|
|TH BMD, g/cm2||0.98±0.14||0.94±0.14||0.88±0.17||0.94±0.16||<0.001|
|EGFR, mL/min/1.73 m2||87.146±14.38||81.08±14.15||60.33±15.63||76.19±18.69||<0.001|
ABI, ankle brachial index; BMD, bone mineral density; LS BMD, BMD in the lumbar spine; FN BMD, femoral neck BMD; TH BMD, total hip BMD; BMI, body mass index; CAVI, cardio-ankle vascular index;CVD, cardiovascular or cerebrovascular disease; DBP, diastolic blood pressure; DM, diabetes mellitus; ESR, erythrocyte sedimentation rate; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; NLR, neutrophil-to-lymphocyte ratio; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; UA, uric acid; WCC, white cell count; eGFR, estimated glomerular filtration rate.
Clinical characteristics of different genders
|Characteristic or parameter||Female (n=214)||Male (n=366)||Total (n=580)||P value|
|Smoking (%)||2 (0.9%)||164 (44.8%)||166 (28.6%)||<0.001|
|CVD (%)||78 (36.4%)||155 (42.3%)||233 (40.2%)||0.188|
|DM (%)||84 (39.3%)||201 (54.9%)||285 (49.1%)||<0.001|
|SBP, mm Hg||131.47±17.66||132.27±17.71||131.97±17.68||0.601|
|DBP, mm Hg||78.65±10.22||83.27±10.35||81.57±10.53||<0.001|
|LS BMD, g/cm2||0.98±0.16||1.17±0.18||1.1±0.2||<0.001|
|FN BMD, g/cm2||0.78±0.13||0.91±0.14||0.86±0.15||<0.001|
|TH BMD, g/cm2||0.84±0.14||0.99±0.14||0.94±0.16||<0.001|
|EGFR, mL/min/1.73 m2||75.3±18.51||76.7±18.79||76.19±18.69||0.385|
ABI, ankle brachial index; BMD, bone mineral density; BMI, body mass index; CAVI, cardio-ankle vascular index; CVD, cardiovascular or cerebrovascular disease; DBP, diastolic blood pressure; DM, diabetes mellitus; ESR, erythrocyte sedimentation rate; FN BMD, femoral neck BMD; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LS BMD, BMD in the lumbar spine; NLR, neutrophil-to-lymphocyte ratio; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; TH BMD, total hip BMD; UA, uric acid; WCC, white cell count; eGFR, estimated glomerular filtration rate.
Bivariate correlation analyses between the covariates and CAVI values
table 3 shows the results of the bivariate correlation analysis. There was a significant positive correlation between CAVI values and age (r=0.631, p<0.001), whereas there was a significant negative correlation between CAVI values and FN BMD (r=−0.229, p<0.001) and TH BMD (r=−0.218, p<0.001). Inflammatory indicators such as NLR (r=0.171, p<0.001), ESR (r=0.185, p<0.001) and fibrinogen (r=0.123, p=0.003) were also positively correlated with CAVI values. Patients with low eGFR had higher CAVI values (r=−0.394, p<0.001). The scatter diagram in figure 1 visually shows the correlations between CAVI values and BMD of the different body areas.
Bivariate correlation analysis between covariate and CAVI
|Characteristic or parameter||r||P value|
ABI, ankle brachial index; BMD, bone mineral density; BMI, body mass index; CAVI, cardio-ankle vascular index;CVD, cardiovascular or cerebrovascular disease; DBP, diastolic blood pressure; ESR, erythrocyte sedimentation rate; FN FN BMD, femoral neck BMD; HDLC, high-density lipoprotein cholesterol; LDLC, low-density lipoprotein cholesterol; LS LS BMD, BMD in the lumbar spine; NLR, neutrophil-to-lymphocyte ratio; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; TH TH BMD, total hip BMD; UA, uric acid; WCC, white cell count; eGFR, estimated glomerular filtration rate.
Regression analysis between CAVI values and BMD
As shown in table 4, model 1 shows the correlation between CAVI values and BMD without adjusting for any confounders. We found that FN BMD and TH BMD were significantly correlated with CAVI values (p<0.001).
Then, in model 2, we adjusted for age, gender, BMI, smoking status, history of CVD and history of DM. In model 3, we adjusted for SBP, HDL-C, blood UA, fibrinogen and eGFR in addition to the variables that were adjusted for in model 2. We found that, after adjusting for related variables, the increase in CAVI values was still correlated with a decrease in BMD. This correlation was statistically significant between CAVI and TH BMD (B=−0.843 (−1.454 to −0.232), p=0.007).
Regression analysis with CAVI as the dependent variable
|Model||Exposure||B (95% CI)||P value||B (95% CI)||P value||B (95% CI)||P value|
|Model 1||TH BMD||−1.812 (−2.475 to −1.149)||<0.001|
|FN BMD||−1.968 (−2.651 to −1.284)||<0.001|
|LS BMD||−0.088 (-0.619 to 0.443)||0.744|
|Model 2||Age||0.064 (0.056 to 0.071)||<0.001||0.064 (0.056 to 0.072)||<0.001||0.066 (0.058 to 0.073)||<0.001|
|Gender||0.252 (0.05 to 0.454)||0.015||0.222 (0.023 to 0.422)||0.029||0.166 (−0.037 to 0.37)||0.109|
|BMI||−0.066 (−0.09 to −0.041)||<0.001||−0.07 (−0.094 to −0.046)||<0.001||−0.072 (−0.097 to −0.048)||<0.001|
|Smoking||0.123 (−0.076 to 0.321)||0.226||0.128 (−0.071 to 0.327)||0.206||0.14 (−0.059 to 0.339)||0.166|
|CVD||0.175 (0.005 to 0.344)||0.044||0.178 (0.008 to 0.348)||0.041||0.185 (0.014 to 0.355)||0.033|
|DM||0.519 (0.354 to 0.684)||<0.001||0.514 (0.349 to 0.679)||<0.001||0.499 (0.334 to 0.664)||<0.001|
|TH BMD||−0.676 (−1.295 to −0.057)||0.032|
|FN BMD||−0.509 (−1.133 to 0.115)||0.109|
|LS BMD||−0.045 (−0.512 to 0.422)||0.849|
|model 3||Age||0.057 (0.047 to 0.067)||<0.001||0.058 (0.047 to 0.068)||<0.001||0.06 (0.05 to 0.07)||<0.001|
|Gender||0.281 (0.067 to 0.494)||0.01||0.228 (0.019 to 0.437)||0.033||0.18 (−0.034 to 0.394)||0.099|
|BMI||0.426 (0.261 to 0.591)||<0.001||0.418 (0.252 to 0.583)||<0.001||0.402 (0.237 to 0.568)||<0.001|
|Smoking||0.115 (−0.079 to 0.309)||0.244||0.123 (−0.072 to 0.318)||0.216||0.135 (−0.06 to 0.33)||0.173|
|CVD||0.168 (0.002 to 0.335)||0.048||0.173 (0.006 to 0.34)||0.043||0.18 (0.013 to 0.348)||0.035|
|DM||0.426 (0.261 to 0.591)||<0.001||0.418 (0.252 to 0.583)||<0.001||0.402 (0.237 to 0.568)||<0.001|
|SBP||0.015 (0.01 to 0.02)||<0.001||0.015 (0.01 to 0.02)||<0.001||0.015 (0.01 to 0.019)||<0.001|
|HDL-C||−0.316 (−0.571 to -0.061)||0.015||−0.322 (−0.578 to −0.066)||0.014||−0.333 (−0.589 to −0.077)||0.011|
|UA||−0.001 (−0.001 to 0)||0.24||−0.001 (−0.001 to 0)||0.285||−0.001 (−0.001 to 0)||0.273|
|fibrinogen||0.002 (−0.005 to 0.008)||0.62||0.002 (−0.004 to 0.008)||0.583||0.002 (−0.004 to 0.008)||0.579|
|eGFR||0.003 (−0.09 to 0.096)||0.945||0.004 (−0.09 to 0.097)||0.935||0.005 (−0.089 to 0.099)||0.917|
|TH BMD||−0.843 (−1.454 to -0.232)||0.007|
|FN BMD||−0.563 (−1.174 to 0.048)||0.071|
|LS BMD||−0.131 (−0.592 to 0.331)||0.579|
Model 1: crude model; model 2: adjusted for age, gender, BMI, smoking, history of CVD and history of DM; model 3: adjusted for age, gender, BMI, smoking, history of CVD, history of DM, SBP, HDL-C and blood UA.
B, unstandardised coefficients; BMD, bone mineral density;BMI, body mass index; CAVI, cardio-ankle vascular index; CVD, cardiovascular or cerebrovascular disease; FN FN BMD, femoral neck BMD;HDLC, high-density lipoprotein cholesterol; LS BMD, BMD in the lumbar spine; SBP, systolic blood pressure; TH BMD, total hip BMD; UA, uric acid; eGFR, estimated glomerular filtration rate.
1. Lian X-L, Zhang Y-P, Li X, et al. . Exploration on the relationship between the elderly osteoporosis and cardiovascular disease risk factors. Eur Rev Med Pharmacol Sci 2017;21:4386–90. [PubMed] [Google Scholar]
2. Said MA, Eppinga RN, Lipsic E, et al. . Relationship of arterial stiffness index and pulse pressure with cardiovascular disease and mortality. J Am Heart Assoc 2018;7 10.1161/JAHA.117.007621 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
3. Oliver JJ, Webb DJ. Noninvasive assessment of arterial stiffness and risk of atherosclerotic events. Arterioscler Thromb Vasc Biol 2003;23:554–66. 10.1161/01.ATV.0000060460.52916.D6 [PubMed] [CrossRef] [Google Scholar]
4. Mattace-Raso FUS, van der Cammen TJM, Hofman A, et al. . Arterial stiffness and risk of coronary heart disease and stroke. Circulation 2006;113:657–63. 10.1161/CIRCULATIONAHA.105.555235 [PubMed] [CrossRef] [Google Scholar]
5. Laurent S, Cockcroft J, Van Bortel L, et al. . Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J 2006;27:2588–605. 10.1093/eurheartj/ehl254 [PubMed] [CrossRef] [Google Scholar]
6. Saiki A, Sato Y, Watanabe R, et al. . The role of a novel arterial stiffness parameter, Cardio-Ankle vascular index (CAVI), as a surrogate marker for cardiovascular diseases. J Atheroscler Thromb 2016;23:155–68. 10.5551/jat.32797 [PubMed] [CrossRef] [Google Scholar]
7. Tanna N, Patel K, Moore AE, et al. . The relationship between circulating adiponectin, leptin and vaspin with bone mineral density (BMD), arterial calcification and stiffness: a cross-sectional study in post-menopausal women. J Endocrinol Invest 2017;40:1345–53. 10.1007/s40618-017-0711-1 [PubMed] [CrossRef] [Google Scholar]
8. Masugata H, Senda S, Inukai M, et al. . Association between bone mineral density and arterial stiffness in hypertensive patients. Tohoku J Exp Med 2011;223:85–90. 10.1620/tjem.223.85 [PubMed] [CrossRef] [Google Scholar]
9. Raggi P, Bellasi A, Ferramosca E, et al. . Pulse wave velocity is inversely related to vertebral bone density in hemodialysis patients. Hypertension 2007;49:1278–84. 10.1161/HYPERTENSIONAHA.107.086942 [PubMed] [CrossRef] [Google Scholar]
10. van den Bos F, Emmelot-Vonk MH, Verhaar HJ, et al. . Links between atherosclerosis and osteoporosis in middle aged and elderly men. J Nutr Health Aging 2018;22:639–44. 10.1007/s12603-018-1039-z [PubMed] [CrossRef] [Google Scholar]
11. Sun C-K. Cardio-ankle vascular index (CAVI) as an indicator of arterial stiffness. Integr Blood Press Control 2013;6:27–38. 10.2147/IBPC.S34423 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
12. Inker LA, Schmid CH, Tighiouart H, et al. . Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 2012;367:20–9. 10.1056/NEJMoa1114248 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
13. Mikumo M, Okano H, Yoshikata R, et al. . Association between lumber bone mineral density and vascular stiffness as assessed by pulse wave velocity in postmenopausal women. J Bone Miner Metab 2009;27:89–94. 10.1007/s00774-008-0014-x [PubMed] [CrossRef] [Google Scholar]
14. Frost ML, Grella R, Millasseau SC, et al. . Relationship of calcification of atherosclerotic plaque and arterial stiffness to bone mineral density and osteoprotegerin in postmenopausal women referred for osteoporosis screening. Calcif Tissue Int 2008;83:112–20. 10.1007/s00223-008-9153-2 [PubMed] [CrossRef] [Google Scholar]
15. Sumino H, Ichikawa S, Kasama S, et al. . Elevated arterial stiffness in postmenopausal women with osteoporosis. Maturitas 2006;55:212–8. 10.1016/j.maturitas.2006.02.008 [PubMed] [CrossRef] [Google Scholar]
16. van Dijk SC, de Jongh RT, Enneman AW, et al. . Arterial stiffness is not associated with bone parameters in an elderly hyperhomocysteinemic population. J Bone Miner Metab 2016;34:99–108. 10.1007/s00774-015-0650-x [PubMed] [CrossRef] [Google Scholar]
17. Liang D-K, Bai X-J, Wu B, et al. . Associations between bone mineral density and subclinical atherosclerosis: a cross-sectional study of a Chinese population. J Clin Endocrinol Metab 2014;99:469–77. 10.1210/jc.2013-2572 [PubMed] [CrossRef] [Google Scholar]
19. Sanyal A, Hoey KA, Mödder UI, et al. . Regulation of bone turnover by sex steroids in men. J Bone Miner Res 2008;23:705–14. 10.1359/jbmr.071212 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
21. Min H, Morony S, Sarosi I, et al. . Osteoprotegerin reverses osteoporosis by inhibiting endosteal osteoclasts and prevents vascular calcification by blocking a process resembling osteoclastogenesis. J Exp Med 2000;192:463–74. 10.1084/jem.192.4.463 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
25. Tanaka Y, Nakayamada S, Okada Y. Osteoblasts and osteoclasts in bone remodeling and inflammation. Curr Drug Targets Inflamm Allergy 2005;4:325–8. 10.2174/1568010054022015 [PubMed] [CrossRef] [Google Scholar]
26. Gao Z, Li X, Miao J, et al. . Impacts of parathyroidectomy on calcium and phosphorus metabolism disorder, arterial calcification and arterial stiffness in haemodialysis patients. Asian J Surg 2019;42:6–10. 10.1016/j.asjsur.2018.04.001 [PubMed] [CrossRef] [Google Scholar]
27. Kim NL, Jang HM, Kim SK, et al. . Association of arterial stiffness and osteoporosis in healthy men undergoing screening medical examination. J Bone Metab 2014;21:133–41. 10.11005/jbm.2014.21.2.133 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
28. Banks LM, Lees B, MacSweeney JE, et al. . Effect of degenerative spinal and aortic calcification on bone density measurements in post-menopausal women: links between osteoporosis and cardiovascular disease? Eur J Clin Invest 1994;24:813–7. 10.1111/j.1365-2362.1994.tb02024.x [PubMed] [CrossRef] [Google Scholar]
More information: Zahra Raisi‐Estabragh et al. Poor Bone Quality is Associated With Greater Arterial Stiffness: Insights From the UK Biobank, Journal of Bone and Mineral Research (2020). DOI: 10.1002/jbmr.4164