Researchers found associations between lower bone mineral density and worse cardiovascular health

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.”

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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.

Study population

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.

Supplementary data

bmjopen-2019-029946supp001.pdf

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.

Clinical characteristics

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.

Biochemical parameters

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.

Supplementary data

bmjopen-2019-029946supp002.pdf

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.

Ethics approval

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.

Statistical analysis

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.

Results
Clinical characteristics
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.

Table 1

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
Age, years	53.33±1.96	62.64±3.04	78.57±6.83	64.82±11.38	<0.001
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
BMI, kg/m2	24.96±3.10	24.68±3.40	24.55±3.78	24.73±3.44	0.48
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
ABI	1.13±0.07	1.14±0.07	1.11±0.08	1.13±0.07	<0.001
CAVI	7.62±0.83	8.23±0.87	9.44±1.363	8.43±1.29	<0.001
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
WCC, 109/L	5.97±1.57	5.70±1.67	5.85±1.47	5.84±1.57	0.24
NLR	2.04±1.02	2.08±1.06	2.72±1.99	2.28±1.46	<0.001
Fibrinogen, g/L	3.22±0.78	3.33±0.89	3.52±0.88	3.36±0.86	0.003
TG, mmol/L	1.80±1.23	1.56±1.09	1.26±0.61	1.54±1.03	<0.001
TC, mmol/L	4.64±0.98	4.52±1.02	3.98±1.06	4.38±1.06	<0.001
HDL-C, mmol/L	1.16±0.29	1.19±0.36	1.11±0.31	1.15±0.32	0.044
LDL-C, mmol/L	2.76±0.84	2.68±0.90	2.29±0.90	2.58±0.90	<0.001
UA, umol/L	369.64±98.05	344.83±100.54	362.26±99.69	359.04±99.79	0.043
ESR, mm/hour	7.98±8.95	8.31±6.70	14.28±13.22	10.22±10.42	<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

Table 2

Clinical characteristics of different genders

Characteristic or parameter	Female (n=214)	Male (n=366)	Total (n=580)	P value
Age, years	65.5±10.97	64.43±11.6	64.82±11.38	0.276
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
BMI, kg/m2	24.14±3.95	25.08±3.06	24.73±3.44	0.003
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
ABI	1.12±0.08	1.13±0.07	1.13±0.07	0.025
CAVI	8.32±1.28	8.49±1.3	8.43±1.29	0.128
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
WCC, 109/L	5.54±1.51	6.01±1.58	5.84±1.57	<0.001
NLR	2.06±1.16	2.41±1.6	2.28±1.46	0.002
Fibrinogen, g/L	3.45±0.78	3.31±0.9	3.36±0.86	0.063
TG, mmol/L	1.37±0.79	1.64±1.14	1.54±1.03	0.001
TC, mmol/L	4.62±1.04	4.25±1.05	4.38±1.06	<0.001
HDL-C, mmol/L	1.26±0.31	1.09±0.31	1.15±0.32	<0.001
LDL-C, mmol/L	2.74±0.92	2.48±0.88	2.58±0.9	0.001
UA, umol/L	311.23±91.81	387±93.56	359.04±99.79	<0.001
ESR, mm/hour	14.05±11.71	8.07±8.96	10.22±10.42	<0.001
EGFR, mL/min/1.73 m2	75.3±18.51	76.7±18.79	76.19±18.69	0.385

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.

Table 3

Bivariate correlation analysis between covariate and CAVI

Characteristic or parameter	r	P value
LS BMD	−0.014	0.744
FN BMD	−0.229	<0.001
TH BMD	−0.218	<0.001
Age	0.631	<0.001
Gender	0.060	0.077
Smoking	0.008	0.825
CVD	0.220	<0.001
DM	0.199	<0.001
BMI	−0.18	<0.001
SBP	0.36	<0.001
DBP	0.108	0.009
ABI	−0.056	0.176
WCC	0.029	0.486
NLR	0.171	<0.001
Fibrinogen	0.123	0.003
TG	−0.102	0.014
TC	−0.198	<0.001
HDL-C	−0.148	<0.001
LDL-C	−0.14	0.001
UA	−0.036	0.384
ESR	0.185	<0.001
EGFR	−0.394	<0.001

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).

able 4

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

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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