The quality of bone and paraspinal muscle in the fragility osteoporotic vertebral compression fracture: a comprehensive comparison between different indicators

doi:10.21203/rs.3.rs-3892580/v1

Download PDF

Research Article

The quality of bone and paraspinal muscle in the fragility osteoporotic vertebral compression fracture: a comprehensive comparison between different indicators

https://doi.org/10.21203/rs.3.rs-3892580/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 15 Jun, 2024

Read the published version in BMC Musculoskeletal Disorders →

Version 1

posted

You are reading this latest preprint version

Purpose: To evaluate the value of five indicators in predicting OVCF through a retrospective case-control study, and explore the internal correlation of different indicators.

Method: We retrospectively enrolled patients over 50 years of age who had been subjected to surgery for fragility OVCF at China Japan Friendship Hospital from January 2021 to September 2023. Demographic characteristics, T-score based on dual-energy X-ray absorptiometry (DXA), CT-based Hounsfield unit (HU) value, vertebral bone quality (VBQ) score based on magnetic resonance imaging (MRI), relative cross-sectional area (rCSA) and the rate of fat infiltration (FI) of paraspinal muscle were collected. A 1:1 age- and sex-matched, fracture-free control group was established from patients admitted to our hospital for lumbar spinal stenosis or lumbar disk herniation.

Results: A total of 78 patients with lumbar fragility OVCF were included. All the five indicators were significantly correlated with the occurrence of OVCFs. Logistic regression analysis showed that average HU value and VBQ score were significantly correlated with OVCF. The area under the curve (AUC) of VBQ score was the largest (0.89). There was a significantly positive correlation between average T-score, average HU value and average total rCSA. VBQ score was significantly positive correlated with FI.

Conclusion: VBQ score and HU value has good value in predicting of fragility OVCF. In addition to bone mineral density, we should pay more attention to bone quality, including the fatty signal intensity in bone and the FI in paraspinal muscle.

osteoporotic vertebral compression fracture

vertebral bone quality score

paraspinal muscle

CT-based Hounsfield unit value

Fragility fractures refer to fractures that occur after no trauma or minor trauma¹. The most common fragility fracture is the osteoporotic vertebral compression fracture (OVCF)². With the aging of the population, the incidence of fragile OVCF is increasing. According to the results of the osteoporosis epidemiological survey in China from 2017 to 2018³, the prevalence of OVCF in the population was 10.5% in men and 9.7% in women over 40 years old. The OVCF is one of the main causes of disability and death in elderly patients, and also causes a heavy burden to the family and society⁴. Therefore, it is of great significance to screen and identify high-risk factors for fragile OVCF in advance and provide timely preventive measures

The most common cause of fragility OVCF is osteoporosis. Currently, dual-energy X-ray absorptiometry (DXA) is the gold standard for assessing bone mineral density (BMD), but its measurements are often overestimated due to lumbar degeneration, such as the formation of vertebral osteophytes⁵. Schuit⁶ found that more than half of all fragility fractures occur in patients diagnosed without osteoporosis by DXA. Therefore, BMD measured by DXA may not be a sensitive indicator for fragility OVCF.

In order to enhance the assessment of vertebral bone quality in patients, numerous indicators have been proposed by scholars, including CT-based Hounsfield unit (HU) value^7,8 and vertebral bone quality score (VBQ) based on magnetic resonance imaging (MRI)^9,10. Compared with DXA, HU value and VBQ score are easy to obtain in clinic, and do not need to add additional examination for patients. The research results of Zou et al. showed that low HU value was related to the occurrence of lumbar OVCF⁷. The research of Li et al. showed that VBQ can be used to predict the occurrence of OVCF¹⁰. Meanwhile, with the recognition of the importance of paraspinal muscle in maintaining lumbar spine stability and reducing lumbar spine load^11,12, researchers^13,14 found that the cross-sectional area (CSA) of paraspinal muscle in OVCF patients was smaller and the rate of fat infiltration (FI) was higher.

Therefore, we intend to evaluate the value of different indicators in predicting OVCF through a retrospective case-control study, and explore the internal correlation of different indicators.

Patients

The study was approved by the ethics review committee of China Japan Friendship Hospital. Because this was a retrospective study, signing the informed consent was waived. We reviewed patients admitted to our department of spinal surgery from January 2021 to September 2023. Inclusion criteria were (1) age $\ge$ 50 years, regardless of the gender; (2) completed perioperative DXA scans, lumbar MRI and lumbar CT scans within 1 weeks; (3) fresh lumbar fragility OVCF; (4) the time between back pain onset and surgery is less than 2 weeks. Exclusion criteria were (1) previous history of fracture, surgery, trauma, or infection in L1−L4; (2) Kummell’s disease; (3) long-term oral hormone-like drug therapy; (4) high-energy vertebral fractures, such as car accidents, falls, etc. In addition, for comparison with patients who did not have fractures, a 1:1 age- and sex-matched, fracture-free control group was established from patients admitted to our hospital for lumbar spinal stenosis or lumbar disk herniation. The exclusion criteria were as described above.

Variables

We collected demographic characteristics of patients via our hospital’s electronic medical record system, including age, sex, body mass index (BMI), course of disease (the time between back pain onset and surgery), and comorbidities, etc.

BMD evaluation

For BMD, DXA scans (Discovery A densitometers, Hologic Inc., Bedford, MA, USA) of lumbar, femur and hip were performed during hospitalization. The T-score of L1-L4 were recorded from DXA. T-score of the fractured vertebrae were discarded.

$$average T-score=\raisebox{1ex}{$T-score of \text{u}\text{n}\text{f}\text{r}\text{a}\text{c}\text{t}\text{u}\text{r}\text{e}\text{d} \text{v}\text{e}\text{r}\text{t}\text{e}\text{b}\text{r}\text{a}\text{e}(\text{L}1-\text{L}4) $}\!\left/ \!\raisebox{-1ex}{$3$}\right.$$

Calculation of the VBQ score

According to the method proposed previousl¹⁵, the VBQ score was calculated using signal intensity (SI) obtained from a non-contrast, T1-weighted lumbar spine MRI image (Fig. 1). Firstly, the mean signal intensity (SI) of the trabecular bone of the vertebrae (L1 − L4) was measured using the midsagittal slices. For patients with abnormalities in the midsagittal slice (eg, hemangioma, venous plexus, scoliosis changes), measurements were performed using the parasagittal slices. The fractured vertebrae was excluded from the calculation and only the remaining vertebrae were used to calculate the VBQ score. The median SI of the L1-L4 was then divided by the SI of the cerebrospinal fluid (CSF) at L3 level to obtain the VBQ score. CSF ROI was placed at L2 or L4 level when L3 CSF space was completely blocked by descending nerve roots.

$$VBQ=\raisebox{1ex}{$Median(SIL1-L4)$}\!\left/ \!\raisebox{-1ex}{$SIL3CSF$}\right.$$

The HU values of vertebrae in CT

The HU values of L1-L4 were measured for each patient according to the method of previous studies (Fig. 2)⁸. An oval region of interest inclusive of trabecular bone was placed in the middle-axial CT image of vertebral body. We avoided placing the ROI near areas that would distort the BMD measurement (posterior venous plexus; focal heterogeneity or lesion, including compression fracture; and imaging-related artifacts).

$$average HU=\raisebox{1ex}{$HU of \text{u}\text{n}\text{f}\text{r}\text{a}\text{c}\text{t}\text{u}\text{r}\text{e}\text{d} \text{v}\text{e}\text{r}\text{t}\text{e}\text{b}\text{r}\text{a}\text{e}(\text{L}1-\text{L}4) $}\!\left/ \!\raisebox{-1ex}{$3$}\right.$$

Paraspinal muscle evaluation on MRI

The Picture Archiving and Communication Systems (PACS) processed imaging data of the paravertebral muscles and measured the cross-sectional area (CSA) of the paraspinal muscles. Relative cross-sectional area (rCSA, the ratio of cross-sectional area of muscle to that of disc at the same level) was introduced for reducing the effect of body shape on muscular parameters. The fat infiltration (FI) of the paraspinal muscles (psoas major [PM], multifidus [MF] and erector spinae [ES]) were measured by Image J softwar. Middle images of each disc space at L1-2, L2 − 3, L3 − 4, and L4 − 5 was selected by locating lines in the sagittal slice. The pseudocoloring technique was used to measure the percentage of fat content. The bright pixels of fatty tissue are painted red and the percentage of red areas in the muscle compartment was calculated¹⁶. The paraspinal muscle-related parameters were the FI averaged on both sides and the three slices and the average total rCSA of PM, MF and ES across different cross sections ¹⁷.

All quantitative measurements were analyzed using the average values of the measurements obtained by the two investigators.

Statistical analysis

Statistical analysis was performed using IBM SPSS Statistics for Windows, version 26.0 (IBMCorp., Armonk, N.Y., USA). The age- and sex-matching process was performed with the case-control matching function of SPSS. Inter-rater and intra-rater reliability was assessed with the intraclass correlation coefficient (ICC), respectively. Paired t test was used to compare the difference between the fracture group and the control group without fractures. The correlation analysis was calculated using Spearman correlation coefficients. Logistic regression analysis was used to examine the contribution of T-score, paravertebral muscle-related parameters, VBQ score and Hu values to the OVCF. Receiver-operator curve (ROC) was used to evaluate the performance of T-score, paravertebral muscle-related parameters, VBQ score and Hu values in distinguishing fracture patients from control patients. Statistical significance was set at P value < 0.05. STATA 15.0 were used to draw the ROC curves.

A total of 78 patients with lumbar fragility OVCF were included in this study, including 28 males and 50 females (Fig. 4). As for the 1:1 age and sex matched patients, all patients with OVCF were successfully matched to patients without fractures, and 78 control patients were also included in the study. There were no significant differences in gender and age between the two groups (Table 1). The ICCs of HU value, VBQ score and paraspinal muscle related parameters were all above 0.8.

Table 1

Patient demographics and OVCF indicators.
	OVCF (n = 78)	non-OVCF (n = 78)	P value
Age	72.21 + 10.53	71.57 + 9.42	0.666
Gender	50 (64.1%)	50 (64.1%)	1
BMI	22.79 + 3.08	22.91 + 2.86	0.806
Hypertension	42	35	0.262
Diabetes	46	37	0.149
Hyperlipidemia	31	38	0.259
Average T	(-2.47 + 0.83)	(-1.34 + 1.44)	< 0.001*
Average HU	66.96 + 10.3	88.74 + 16.32	< 0.001*
VBQ	4.13 + 0.34	3.06 + 0.39	< 0.001*
Average total rCSA	2.48 + 0.29	2.69 + 0.44	0.001*
Average FI	16.18 + 1.14	17.18 + 0.95	< 0.001*

The average T-score, VBQ score and average FI in the OVCF group were significantly higher than those in the control group, and HU value and average total rCSA were significantly lower than those in the control group. T-score, average HU value, VBQ score, average total rCSA and average FI were all significantly correlated with the occurrence of OVCFs. The correlations were ranked from high to low as VBQ score, average HU value, average FI, T-score, and average total rCSA (Rho = 0.84, -0.665, 0.568, -0.399, -0.297, respectively; Table 2). After adjusting for age, sex and BMI, logistic regression analysis showed that HU value and VBQ score were significantly correlated with OVCF (OR = 0.39, 95%CI: 0.29–0.77; and OR = 2.98, 95%CI: 1.78–8.25, respectively; Table 2). ROC curve was used to evaluate the different indicators for predicting OVCF. The area under the curve (AUC) of VBQ score was the largest (0.895, Fig. 5).

Table 2

Sperman correlation analysis and logistics regression used to evaluate the correlation between the five indicators and OVCF.
	Rho	P	OR	95% CI	P
T-score	-0.399	< 0.001*	0.533	0.37–1.13	0.08
HU	-0.665	< 0.001*	0.39	0.29–0.77	0.02*
VBQ	0.84	< 0.001*	2.98	1.78–8.25	0.007*
CSA	-0.297	0.007*	0.69	0.52–1.05	0.91
FI	0.568	< 0.001*	1.47	0.85–2.3	0.18

The correlation analysis among the different indicators found that there were significant correlations among the five indicators (Table 3). There was a positive correlation between average T-score, HU value and average total rCSA. VBQ score was significantly positive correlated with FI. The strongest correlations were HU value and average T-score (Rho = 0.926), VBQ score and average FI (Rho = 0.919), respectively.

Table 3

Sperman correlation analysis used to evaluate the correlation between the five indicators.
	T-score	HU	VBQ	rCSA	FI
T-score		0.926	-0.82	0.679	-0.759
HU	0.926		-0.823	0.625	-818
VBQ	-0.82	-0.823		-0.634	0.919
rCSA	0.679	0.625	-0.634		-0.74
FI	-0.759	-818	0.919	-0.74

In the face of an increasing number of risk indicators for OVCF, we compared five indicators that have been widely studied firstly. Through the retrospective case-control study, we found that all five indicators were associated with OVCF, and the two most strongly correlated indicators were VBQ score and HU value. At the same time, the internal correlation analysis showed that T-score, HU value and rCSA were significantly positive correlated, and FI and VBQ score were significantly positive correlated.

It is of great significance to screen and identify high-risk factors for fragile OVCF in advance and provide timely preventive measures. The most common cause of fragility OVCF is osteoporosis. Currently, dual-energy X-ray absorptiometry (DXA) is the gold standard for assessing bone mineral density (BMD), but its measurements are often overestimated due to lumbar degeneration, such as the formation of vertebral osteophytes⁵. With increasing awareness of identifying high risk factors for OVCF, it has been identified based on some routine tests. In 2013, it was found that CT-based HU value could be used to determine BMD⁸. A number of studies^7,18,19 by Li have shown that HU value has good application value in identing the high-risk factors of OVCF and postoperative nonunion. A wide range of studies have confirmed that HU value can be used as a simple and important method to evaluate BMD in patients¹⁹.

VBQ score based on MRI is a new technique to evaluate bone quality first introduced by Ehresman in 2019²⁰, and its principle is to measure the fat content of the vertebra and indirectly reflect the bone quality²⁰. A good correlation between the VBQ score and BMD has been widely demonstrated²¹. It has also been found to predict complications after spinal surgery.²²A retrospective single-center cohort study also found that the VBQ score could predict fragile vertebral fractures independently of BMD²³.

With the deepening of clinical research, clinicians gradually discovered the important role of paraspinal muscle. A large number of studies have shown that the reduction of paraspinal muscle CSA and the increase of FI are closely related to the occurrence of various lumbar diseases^14,17,24. Paraspinal muscle-related parameters were significantly correlated with BMD¹¹. Paraspinal muscle degeneration can be used to predict OVCF¹⁴, postoperative outcome of lumbar stenosis²⁴, postoperative complications of scoliosis¹⁷, etc. The clinical outcome of patients can be improved by strengthening the paraspinal muscle²⁵.

Each indicator has been validated in clinical studies for its important role in assessing risk factors for OVCF. But which indicator is better, the internal correlation between each indicator is still questionable. In our study cohort, VBQ score and HU value ranked first and second in evaluating OVCF risk, superior to T-score. Therefore, in clinical work, we recommend the priority selection of VBQ score as an evaluation indicator for OVCF risk. If the patient cannot complete MRI examination, we suggest that HU value should be selected as an evaluation indicator to identify the risk of OVCF. Compared with DXA, CT or MRI can also provide clinical information in addition to bone quality. At the same time, Ehresman’s study²³ showed that the fatty signal intensity in bone has an important influence on bone quality. In our study, we also found that in addition to BMD, the fatty signal intensity in bone may be more important.

In the evaluation of paraspinal muscle, we found that FI was more important than CSA. Other studies¹⁴ have come to similar conclusions that OVCF is more related to FI than CSA. We have chosen rCSA to reduce the interference caused by factors such as height, but the conclusion is still consistent. The possible reason is that greater CSA does not mean stronger paraspinal muscle strength, and the correlation between paraspinal muscle strength and FI is stronger. At the same time, we found that the correlation between OVCF and FI or CSA was not as good as that of VBQ score, HU value or T-score. This suggests that OVCF may be more related to bone quality rather than paraspinal muscles. Finite element analysis found that strengthening the paravertebral muscle could only reduce the vertebral load by 3.1% at most¹². Therefore, as for the prevention of OVCF, the most noteworthy is the bone quality. The paraspinal muscle is also a very critical intervention variable.

As for the internal correlation of different indicators, we found that T-score was significantly correlated with HU value, and VBQ score was significantly correlated with FI. The results from other studies^7,11 also show that T-score and HU are significantly consistent. The measurement of the two depends on bone trabecular density, etc. So in the clinic, HU value may replace T-score to assess the BMD. Li 's study¹⁶ showed that the CSA and FI of the paraspinal muscle may be more likely to influence the value of the VBQ score than BMD, which showed the important influence of the fatty signal intensity in bone quality.

The limitations of this study should also be considered. First, as a retrospective study, the data were obtained from the medical record system. Some data, such as patients' special comorbidities, smoking or drinking history, were not fully recorded. Some studies have proved that these comorbidities might affect the occurrence of OVCF. Second, patients in the control group were hospitalized for surgical treatment due to lumbar spinal stenosis or lumbar disc herniation. Lumbar MRI scans are less common in patients treated conservatively. Therefore, VBQ scores and the other indicators in patients who did not receive any surgical treatment were not analyzed in this study. In addition, the acquisition of HU value, VBQ score and paraspinal muscle-related parameters differ greatly. Although there is good internal consistency in our study, this is based on sufficient sample size training and internal communication among data collectors.

VBQ score and HU value has good value in predicting of fragility OVCF. The higher the VBQ score, the lower the HU value, the greater the likelihood of a fragility OVCF. In addition to BMD, we should pay more attention to bone quality, including the fatty signal intensity in bone and the FI in paraspinal muscle.

Achnowledgements

None

Achnowledgements

None

Funding

This study was funded by Beijing Natural Science Foundation number L212005.

Conflict of interest

The authors declare no conflicting interests.

Ethics approval

The study was approved by the ethics committee of China Japan Friendship Hospital.

Consent to participate

Not Applicable

Consent for publication

Not Applicable

Author Contribution

P.Y were responsible for the study design. SZ. Z and HN. M contributed to the acquisition and analysis of data. SZ.Z, XG.D and P.Y contributed to the interpretation and analysis of data. SZ.Z were responsible for drafting the article. P.Y were responsible for revising it critically for important intellectual content. P.Y have approved the version to be published and all subsequent versions. All authors read and approved the final manuscript.

Cheng X, Zhao K, Zha X, et al. Opportunistic Screening Using Low-Dose CT and the Prevalence of Osteoporosis in China: A Nationwide, Multicenter Study. Journal of bone and mineral research: the official journal of the American Society for Bone and Mineral Research. 2021;36(3):427–435.
LeBoff MS, Greenspan SL, Insogna KL, et al. The clinician's guide to prevention and treatment of osteoporosis. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2022;33(10):2049–2102.
Society TOFTGoOCoCG. Treatment guidelines for osteoporotic vertebral compression fractures. Chin J Osteoporos. 2015;6(21):643–648.
Papaioannou A, Santesso N, Morin SN, et al. Recommendations for preventing fracture in long-term care. CMAJ: Canadian Medical Association journal = journal de l'Association medicale canadienne. 2015;187(15):1135–1144.
Muraki S, Yamamoto S, Ishibashi H, et al. Impact of degenerative spinal diseases on bone mineral density of the lumbar spine in elderly women. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2004;15(9):724–728.
Schuit SC, van der Klift M, Weel AE, et al. Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone. 2004;34(1):195–202.
Zou D, Ye K, Tian Y, et al. Characteristics of vertebral CT Hounsfield units in elderly patients with acute vertebral fragility fractures. Eur Spine J. 2020;29(5):1092–1097.
Pickhardt PJ, Pooler BD, Lauder T, del Rio AM, Bruce RJ, Binkley N. Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Annals of internal medicine. 2013;158(8):588–595.
Li W, Zhu H, Liu J, Tian H, Li J, Wang L. Characteristics of MRI–based vertebral bone quality scores in elderly patients with vertebral fragility fractures. Eur Spine J. 2023;32(7):2588–2593.
Li R, Yin Y, Ji W, et al. MRI-based vertebral bone quality score effectively reflects bone quality in patients with osteoporotic vertebral compressive fractures. Eur Spine J. 2022;31(5):1131–1137.
Han G, Zou D, Liu Z, et al. Paraspinal muscle characteristics on MRI in degenerative lumbar spine with normal bone density, osteopenia and osteoporosis: a case-control study. BMC Musculoskelet Disord. 2022;23(1):73.
Kang S, Chang MC, Kim H, et al. The Effects of Paraspinal Muscle Volume on Physiological Load on the Lumbar Vertebral Column: A Finite-Element Study. Spine. 2021;46(19):E1015-e1021.
Huang CWC, Tseng IJ, Yang SW, Lin YK, Chan WP. Lumbar muscle volume in postmenopausal women with osteoporotic compression fractures: quantitative measurement using MRI. Eur Radiol. 2019;29(9):4999–5006.
Chen Z, Shi T, Li W, Sun J, Yao Z, Liu W. Role of paraspinal muscle degeneration in the occurrence and recurrence of osteoporotic vertebral fracture: A meta-analysis. Front Endocrinol (Lausanne). 2022;13:1073013.
Ehresman J, Pennington Z, Schilling A, et al. Novel MRI-based score for assessment of bone density in operative spine patients. The spine journal: official journal of the North American Spine Society. 2020;20(4):556–562.
Li W, Wang F, Chen J, Zhu H, Tian H, Wang L. MRI–based vertebral bone quality score is a comprehensive index reflecting the quality of bone and paravertebral muscle. The spine journal: official journal of the North American Spine Society. 2023.
Leng J, Han G, Zeng Y, Chen Z, Li W. The Effect of Paraspinal Muscle Degeneration on Distal Pedicle Screw Loosening Following Corrective Surgery for Degenerative Lumbar Scoliosis. Spine. 2020;45(9):590–598.
Wang H, Zou D, Sun Z, Wang L, Ding W, Li W. Hounsfield Unit for Assessing Vertebral Bone Quality and Asymmetrical Vertebral Degeneration in Degenerative Lumbar Scoliosis. Spine. 2020;45(22):1559–1566.
Zou D, Sun Z, Zhou S, Zhong W, Li W. Hounsfield units value is a better predictor of pedicle screw loosening than the T-score of DXA in patients with lumbar degenerative diseases. Eur Spine J. 2020;29(5):1105–1111.
Ehresman J, Schilling A, Pennington Z, et al. A novel MRI-based score assessing trabecular bone quality to predict vertebral compression fractures in patients with spinal metastasis. Journal of neurosurgery Spine. 2019:1–8.
Salzmann SN, Okano I, Jones C, et al. Preoperative MRI-based vertebral bone quality (VBQ) score assessment in patients undergoing lumbar spinal fusion. The spine journal: official journal of the North American Spine Society. 2022;22(8):1301–1308.
Li W, Zhu H, Hua Z, et al. Vertebral Bone Quality Score as a Predictor of Pedicle Screw Loosening Following Surgery for Degenerative Lumbar Disease. Spine. 2023;48(23):1635–1641.
Ehresman J, Schilling A, Yang X, et al. Vertebral bone quality score predicts fragility fractures independently of bone mineral density. The spine journal: official journal of the North American Spine Society. 2021;21(1):20–27.
Han G, Zou D, Li X, et al. Can fat infiltration in the multifidus muscle be a predictor of postoperative symptoms and complications in patients undergoing lumbar fusion for degenerative lumbar spinal stenosis? A case-control study. Journal of orthopaedic surgery and research. 2022;17(1):289.
Zhao H, He Y, Yang JS, et al. Can paraspinal muscle degeneration be a reason for refractures after percutaneous kyphoplasty? A magnetic resonance imaging observation. Journal of orthopaedic surgery and research. 2021;16(1):476.

No competing interests reported.

Download PDF

Journal Publication

published 15 Jun, 2024

Read the published version in BMC Musculoskeletal Disorders →

Version 1

posted

You are reading this latest preprint version

The quality of bone and paraspinal muscle in the fragility osteoporotic vertebral compression fracture: a comprehensive comparison between different indicators

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Method

Patients

Variables

BMD evaluation

Calculation of the VBQ score

The HU values of vertebrae in CT

Paraspinal muscle evaluation on MRI

Statistical analysis

Results

Discussion

Conclusion

Achnowledgements

Declarations

Author Contribution

References

Additional Declarations

Status:

Journal Publication

Version 1