Association of liver multi-parameter quantitative metrics determined by dual-layer spectral detector computed tomography (SDCT) with coronary plaque scores

Introduction

Cardiovascular-related diseases are the leading cause of global increase in morbidity and mortality (1,2). Although primary and secondary prevention have been optimised in accordance with guidelines, some major adverse cardiovascular events (recurrent angina pectoris, acute myocardial infarction, severe arrhythmia, heart failure, and death due to coronary heart disease) continue to occur (3,4). The metabolic risk associated with cardiovascular disease has received increasing attention in clinical settings. Among the many cardiovascular metabolic risk factors, fat metabolism and inflammatory disorders caused by increased hepatic fat deposition are important contributors not only in the formation of atherosclerotic plaques but also in plaque rupture, triggering adverse cardiovascular events (5-11). Therefore, assessing the degree of hepatic steatosis is crucial for monitoring the occurrence and progression of coronary atherosclerosis and for the primary and secondary prevention of coronary artery disease (CAD).

Hsu et al. (12) analyzed the correlation between the degree of hepatic steatosis and coronary plaque characteristics. The study showed that hepatic steatosis is associated with the presence of non-calcified and mixed plaques in the coronary arteries, and the severity of coronary obstruction and the development of multi-vessel atherosclerosis are closely related to the severity of hepatic steatosis (13). However, previous studies generally have limitations in the precise and non-invasive diagnosis of hepatic steatosis. Traditional B-mode ultrasound assessment is subjective and exhibits limited sensitivity for the diagnosis of mild steatosis (14,15). The measurement of proton density fat fraction (PDFF) values based on magnetic resonance is not suitable for routine clinical screening and longitudinal assessment of hepatic steatosis (16). Currently, traditional computed tomography (CT) is widely used in clinical practice; however, its reliance on the attenuation imaging of mixed-energy X-rays restricts its capacity to identify material composition and diagnostic sensitivity (17). Dual-layer spectral detector computed tomography (DL-SDCT) is a new type of spectral CT technology that can provide virtual single-level imaging and effective atomic number imaging, enhancing the differentiation of tissue composition compared to conventional CT (18,19). Given that hepatic steatosis is an independent risk factor for cardiovascular diseases, precise detection of hepatic steatosis is expected to provide a new perspective for the prevention and treatment of CAD.

Coronary computed tomography angiography (CCTA) can be used to assess different stages of coronary atherosclerosis, from the detection of early subclinical disease to the evaluation of acute chest pain. In addition, CCTA can non-invasively quantify plaque burden, which is helpful for disease diagnosis, prognosis, and treatment (20). In recent years, the segmental involvement score (SIS) and the segmental stenosis score (SSS) have been used to evaluate the extent and severity of coronary artery plaques (21,22). However, there have been few previous studies on the relationship between hepatic steatosis and the overall plaque burden (23). Therefore, the purpose of this study was to evaluate the association between liver multiparameter metrics measured by spectral detector computed tomography (SDCT) and the severity and extent of coronary artery plaque assessed by CCTA plaque scores, achieving the screening and primary prevention of high-risk populations of coronary heart disease. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-53/rc).

Methods

Study participants

Between October 2021 and August 2022, we assessed 2,421 patients suspected of CAD due to symptoms of chest tightness or pain who underwent CCTA at our hospital. Among these patients, 123 with a history of percutaneous coronary intervention or coronary artery bypass graft, 479 with incomplete clinical and laboratory data, 710 without abdominal spectral CT examination, 186 with a history of drinking and liver viral infection, and 279 with normal coronary arteries were excluded; 644 meeting the requirements were included in our study. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of Shengjing Hospital of China Medical University (No. 2022PS958K) and informed consent was taken from all individual participants. A flowchart of patient enrolment and study design is shown in Figure 1.

Figure 1 Flowchart of patient enrollment and the study design. CCTA, coronary computed tomography angiography; PCI, percutaneous coronary intervention; CABG, coronary-artery-bypass-grafting; SDCT, spectral detector computed tomography; SIS, segmental involvement score; SSS, segmental stenosis score.

Information on the presence of cardiac risk factors for all subjects was compiled, drawing on their medical histories and case data retrieved from the hospital information system. Hypertension (HTN) was defined as a history of the disease or antihypertensive medicine consumption. Diabetes mellitus (DM) was defined as a previous diagnosis of DM or the use of hypoglycaemic medications. Current smoking or smoking cessation within 1 month of CCTA was defined as a history of smoking. Patients’ laboratory data, including total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) levels, were measured within 1 month before and after CT scanning; if multiple measurements were taken, the laboratory data closest to the date of CCTA examination were considered.

Spectral CT scanning and post-processing

All scans were performed using a 64-slice dual-spectral detector CT scanner (IQon Spectral CT; Philips Healthcare, Best, Netherlands). Patients with a heart rate ≥70 bpm were administered an oral β-blocker (25–50 mg, metoprolol succinate sustained-release tablets, AstraZeneca, Sweden) to control heart rate prior to the examination. All sections from 1 cm below the bifurcation of the trachea to the base of the heart were scanned using the retrospective electrocardiogram (ECG)-gated axial scanning mode. The coronary CTA scan parameters were as follows: tube voltage, 120 kVp; tube current, automatic exposure control (dose right index =13); field of view, 220 mm; matrix, 512×512; tube rotation time, 0.27 s; detector collimation, 64×0.625 mm; slice thickness, 0.9 mm; and slice interval, 0.45 mm. Iohexol (350 mg/mL, GE Healthcare, Ireland) was used as the contrast agent (total amount of contrast agent = body weight × 0.8 mL/kg, injection flow rate (mL/s) = total amount (mL)/injection time (6 s), and then 20–30 mL of normal saline was injected at the same injection rate. Bolus Tracker contrast agent tracking technology was applied to the region of interest (ROI) in the ascending aorta at the pulmonary artery level, starting the scan 6 s after triggering after reaching a predetermined signal attenuation threshold of 90 Hounsfield units (HU). The average radiation dose for CCTA examination is 11.2 mSv.

The SDCT liver scan range for all upper abdominal CT scans spanned from the liver apex to 1 cm below the caudal end of the kidneys; this was performed with an inspiratory breath hold. Scanning parameters included a tube voltage of 120 kV; automatic tube current modulation technique [dose right index =22 reference (Ref): 162 mAs]; rotation time, 0.75 s; pitch, 1.172:1; and image thickness, 3.0 mm, the average radiation dose for a non-contrast SDCT scan of the upper abdomen is 4.5 mSv. The raw data were reconstructed using a spectral iterative reconstruction algorithm (Level-3) to generate spectral-based image datasets. These datasets comprised true conventional polychromatic (120 kVp) images as well as various spectral reconstructions such as virtual monoenergetic images (VMI; energy level at 40 keV), Z-effective (Zeff) images- are colour-coded based on the effective atomic number of tissues, and the slope of the energy spectrum curve of the liver tissue [λ HU, λ HU = (CT_{40 kev} − CT_{70 kev})/30]. Circular ROIs in the right lobe of the liver, approximately 300 mm² (deviation <10 mm²), were drawn while avoiding intrahepatic blood vessels, bile duct structures, focal liver lesions, and imaging artefacts. Two experienced radiologists (M.W. and R.B.) with 6 and 8 years of experience, respectively, measured the SDCT parameters while blinded to the clinical data. The final measurements were calculated as the mean values of the two observers after ensuring consistency. Figure 2 shows a multiparameter image from the spectral base image data package reconstructed from a patient’s liver SDCT images.

Figure 2 Liver SDCT multi-parameters images (transverse CT sections; 3-mm thick) from a 65-year-old female. The size of the ROI was set to 300 mm². Liver circular ROI is located at the level of portal right branch emanating from the main portal vein, outlined with a white solid line. The spleen circular ROI is delineated at the same axial level, for the purpose of calculating L/S ratio. (A) CT_{40 kev} image, CT_{40 kev} =34.90 HU, (B) Zeff image, Zeff =7.03, (C) CT_poly image, CT_poly =44.7 HU, L/S ratio =0.93, (D) λ image, λ=−0.36. SDCT, spectral detector computed tomography; CT, computed tomography; ROI, region of interest; L/S, liver/spleen; CT_{40 kev}, liver fat attenuation assessed by virtual mono-energetic images at 40 kev; HU, Hounsfield unit; Zeff, effective atomic number; CT_poly, liver fat attenuation assessed by polychromatic imaging.

Group standard

We assessed each coronary artery using two coronary plaque scoring methods with CCTA: SSS and SIS (21,22). We defined the cut-off of CCTA scores based on data from a previous report: SSS ≥5 and SIS ≥5 (21). Figure 3 shows the SIS and SSS integration rules for each patient.

Figure 3 Coronary atherosclerotic plaque analysis. Images from a 69-year-old male who presented with atypical chest pain. (A) Three-dimensional volume rendering technique reconstruction from CCTA, (B) CPR of the LAD, (C) probe images of the proximal and middle segments of the LAD plaque, (D) CPR of the RCA, (E) probe images of the proximal plaque in the RCA. (B,D) Depict the corresponding coronary artery branches with different plaques outlined by dotted yellow boxes, while (C,E) show the probe images of the corresponding plaques outlined by solid yellow boxes. As shown in picture, CCTA identified a calcified plaque with mild luminal narrowing in the proximal segment of the LAD, SIS =1, SSS =1; another mixed plaque with moderate luminal narrowing was identified in the middle segment of the LAD, SIS =1, SSS =2; a non-calcified plaque with minimal luminal narrowing was identified in the proximal segment of the RCA, SIS =1, SSS =0. Therefore, the patient’s total SIS was 3 (SIS =3) and the total SSS was 3 (SSS =3). CCTA, coronary computed tomography angiography; CPR, curved multiplanar reconstruction; LAD, left anterior descending; RCA, right coronary artery; SIS, segment involvement score; SSS, segment stenosis score.

Statistical analysis

All statistical analyses were performed using the commercially available SPSS Statistics V 26.0 (IBM Corporate, New York, USA) software. Continuous variables were summarised as means ± standard deviation (SD) or median with the 25th and 75th percentiles of the interquartile range, as appropriate. Categorical variables were expressed as counts and percentages. The intraclass correlation coefficient (ICC) and Bland-Altman plots were calculated to assess inter-observer agreement for the two measurements, and the mean of the measured values was taken as statistical data for further analysis. The clinical characteristics of the subjects in the two groups were compared using a two-sample t-test or Mann-Whitney U-test. Categorical variables (e.g., sex) were compared using the Chi-squared test. Multivariate logistic regression models were used to assess the predictors of higher coronary plaque scores. Model 1 was adjusted for age, sex, smoking, HTN, hyperlipidaemia, and DM. Model 2 was adjusted for all the covariates in Model 1, including waist circumference, body mass index, and visceral adipose tissue (VAT)/subcutaneous adipose tissue (SAT). The strength of the associations is presented as odds ratios [ORs, 95% confidence interval (CI)]. Statistical significance was set at P<0.05.

Results

Demographic and clinical characteristics

A total of 644 consecutive participants participated in this study. Table 1 shows the characteristics and laboratory data of the enrolled patients. In the SIS and SSS groups, there were significant differences in age (P=0.011, <0.001, respectively), sex (P<0.001, <0.001, respectively), waist (P<0.001, 0.004, respectively), cholesterol levels (P=0.003, 0.001, respectively), HDL cholesterol levels (P<0.001, 0.003, respectively), and LDL cholesterol levels (P=0.008, 0.003, respectively). In the high SIS and SSS groups, the body mass index (BMI) was slightly higher than that in the low plaque burden group; however, the difference was significant in the SIS group (P=0.037), whereas it did not differ between the different SSS groups (P=0.228).

Table 2 shows the differences in liver spectral multiparameter images, conventional polychromatic images, and composition of visceral and subcutaneous fat according to the extent and severity of coronary plaque involvement. CT_{40 kev}, CT_{70 kev}, Zeff, λ, VAT, and SAT were significantly different among populations in different SIS and SSS groups (All P<0.001), while CT_poly was statistically significant in different SIS groups (P=0.045); there was no significant difference in SSS groups (P=0.115).

Measurement agreement assessment

Excellent interobserver agreement was observed for CT_{40 kev} measurements (ICC =0.960; 95% CI: 0.954–0.966), CT_{70 kev} (ICC =0.931; 95% CI: 0.920–0.941), Zeff (ICC =0.937; 95% CI: 0.926–0.945), and CT_poly (ICC =0.916; 95% CI: 0.902–0.927). We also analysed the systematic bias of different CT parameters between the two viewers (Figure 4). Bland-Altman plots revealed that there was no statistically significant bias among all the quantitative parameters, and the number with large deviations was controlled within 5%. Therefore, the mean values of the two measurements were used for all calculations and statistical comparisons.

Figure 4 Scatterplots for bias of multi parameter quantitative metrics of liver. (A) Bland-Altman plot of CT_{70 kev} for two viewers. (B) Bland-Altman plot of CT_{40 kev} for two viewers. (C) Bland-Altman plot of CT_poly for two viewers. (D) Bland-Altman plot of Zeff for two viewers. SD, standard deviation; CT_{40 kev}, liver fat attenuation assessed by virtual mono-energetic images at 40 kev; CT_{70 kev}, liver fat attenuation assessed by virtual mono-energetic imaging at 70 kev; CT_poly, liver fat attenuation assessed by polychromatic imaging; Zeff, effective atomic number.

Screening of the most powerful quantitative parameters of the liver

On multivariate binary logistic analysis, after adjusting the conventional cardiovascular risk factors (age, sex, BMI, DM, HTN, smoking, drinking, TC, TG, HDL, and LDL), CT_{40 kev}, CT_{70 kev}, λHU, Zeff, and CT_poly were found to be significantly associated with high SIS and SSS (Table 3). This is due to the collinearity between the above-mentioned hepatic steatosis-related parameters. We substituted these parameters into the regression equation for analysis. According to the standard partial regression coefficient, among these parameters, Zeff was the most powerful independent indicator, whether in the SIS (standard partial regression coefficient =−0.422, P<0.001) or SSS groups (standard partial regression coefficient =−0.346, P<0.001). Thus, Zeff was selected as the best quantitative indicator of the changes in liver parenchyma and was divided into four parts according to the quartile: Zeff ≤7.00 was defined as the Q4 quartile; 7.00< Zeff ≤7.08, Q3 quartile; 7.08< Zeff ≤7.14, Q2 quartile; and Zeff >7.14, Q1 quartile.

Association between liver quantitative parameters and coronary plaque scores

We analyzed the predictive value of the liver SDCT quantitative parameter Zeff for high coronary plaque scores (SSS ≥5, SIS ≥5) (Table 4). To mitigate the confounding bias introduced by multiple metabolic risk factors, we conducted an assessment using adjusted models. In logistic regression analysis, continuous variables can be converted into binary categorical variables by determining the optimal binary classification threshold through the calculation of the corresponding Youden’s index. In multivariate analysis adjusted for age, sex, VAT/SAT ratio, and traditional CAD risk factors, the liver Zeff distribution was also an independent predictor of high coronary plaque scores. Compared with patients in the lowest quartile of Zeff, the adjusted ORs and 95% CIs for SIS were 2.116 (1.134–3.949), 2.832 (1.461–5.491), and 3.584 (1.857–6.918) for those in the second, third, and fourth quartiles, respectively. The corresponding ORs (95% CI) for SSS were 1.933 (1.040–3.592), 2.900 (1.499–5.609), and 3.368 (1.743–6.510) for the upper three quartiles of Zeff.

In addition, age, sex, and visceral fat proportion were independent risk factors for high SIS and SSS (All P<0.05). Further subgroups were analysed, as shown in the forest plots of Figures 5,6. After adjusting for CAD risk factors such as HTN, hyperlipidaemia, DM, waist circumference, BMI, and smoking, SDCT-Zeff was still an independent influencing factor for the high coronary plaque scores, especially among those who were <60 years old and male and had VAT/SAT <1.18.

Figure 5 Multivariable regression analysis of high SIS score with liver-Zeff value and risk factors in subgroup. yr, years; OR, odds ratio; CI, confidence interval; Zeff, effective atomic number; Ref, reference; VAT, visceral adipose tissue; SAT, subcutaneous adipose tissue; SIS, segment involvement score.

Figure 6 Multivariable regression analysis of high SSS score with liver-Zeff value and risk factors in subgroup. yr, years; OR, odds ratio; CI, confidence interval; Zeff, effective atomic number; Ref, reference; VAT, visceral adipose tissue; SAT, subcutaneous adipose tissue; SSS, segment stenosis score.

Discussion

Our study demonstrates the association between coronary atherosclerosis plaque scores and liver spectral multi-parameter quantitative metrics (CT_{40 kev}, CT_{70 kev}, λ, Zeff), as determined by SDCT. Compared with patients in the lowest quartile of Zeff, those in the second, third, and fourth quartiles exhibited a higher risk of high coronary plaque burden, especially among those <60 years old, males, and with VAT/SAT <1.18.

Previous studies have predominantly employed obesity as a maker of metabolic dysregulation, utilizing metrics such as BMI, waist circumference, and waist-to-hip ratio to quantify obesity and the risk of coronary heart disease. However, the concept of the ‘obesity paradox’ has complicated the relationship between obesity and the prognosis of coronary heart disease (24-26). The concept of visceral obesity/ectopic fat (hepatic fat, skeletal muscle fat) may provide clues to the obesity paradox in cardiology, as both are involved in the pathophysiological processes of diabetes, insulin resistance, and obesity-related diseases. Therefore, visceral fat and hepatic fat are considered two key drivers of cardiometabolic risks associated with specific levels of body fat (27). The liver, being the most sensitive and initial organ in systemic metabolic dysregulation, when exhibiting ectopic fat deposition, signifies that subcutaneous fat stores are unable to retain excess energy, impairing insulin sensitivity and the body’s ability to rapidly store fat to reduce postprandial blood lipid levels increases. This weakens the inhibitory effect on the deleterious impact of postprandial blood lipid levels on the development of atherosclerosis, thereby accelerating the onset and progression of atherosclerosis (28). Additionally, liver-induced metabolic disorders can exacerbate cardiovascular endothelial damage through various pathophysiological mechanisms, including oxidative stress, inflammation, and endothelial dysfunction (10,11). Therefore, considering the impact of adipose tissue metabolism on the development and progression of atherosclerosis, the assessment of the degree of ectopic fat deposition in the liver is more critical than the assessment of individual obesity indicators. Ultrasound and magnetic resonance imaging (MRI) are two conventional imaging modalities for detecting hepatic fat accumulation (15,29). However, ultrasound results may be influenced by the operator’s experience and instruments. MRI is expensive and time-consuming, limiting its clinical applicability. Although conventional CT is frequently used, its sensitivity is limited. Choi et al. (30) indicated that low-kVp unenhanced CT can effectively diagnose hepatic steatosis while reducing radiation exposure. In comparison, SDCT, as a new imaging technology, can provide more spectral imaging information than conventional CT (31-34). In addition, many studies have shown that the energy spectral indicators of spectral CT have a significant correlation with pathological fat deposition in the liver (35,36). Thus, SDCT multiparameter quantitative metrics may better capture early pathological changes in the liver parenchyma.

Consistent with the findings of previous study (37), patients with metabolic abnormalities were more likely to develop coronary plaques in our study. However, after adjusting for common cardiovascular risk factors, the categorization of Zeff based on SDCT remained an independent risk factor for the extent and severity of coronary plaque involvement, especially among those <60 years, males and those with a VAT/SAT <1.18. Given SDCT imaging properties, Zeff is more sensitive to the change in fat deposition in the liver than other collinear indicators; a lower Zeff value means more fatty infiltration in the liver. This condition is more prone to triggering metabolic disorders, such as hyperlipidaemia, insulin resistance, and aggravation of systemic low-grade inflammatory levels; however, these factors also damage the cardiovascular endothelium (38,39). Additional metabolic risk factors are more frequently present in older adults owing to their impaired metabolic performance (40); thus, the damage to the cardiovascular endothelium caused by hepatic steatosis may be masked by other stronger coronary risk factors. Previous research indicates that women with obesity and type 2 DM lose their cardioprotective effects (41), and microvascular dysfunction is more common in women (42). Therefore, men also tend to have fewer metabolic risk factors than women, which could account for the stronger correlation we found in our study between the quantitative liver parameters and high coronary plaque scores in male. At the same time, in the lower VAT/SAT ratio subgroup, cardiovascular risk factors such as older age (48.9% vs. 53.3%) and DM (21.4% vs. 22.6%) accounted for a relatively small proportion, making the coronary plaque burden caused by liver lipid metabolism disorders appear more prominent. Previous study indicated that the SIS and SSS are associated with the long-term poor prognosis following coronary heart disease (21), and Tanaka et al. (28) demonstrated that higher SAT and lower VAT were inversely correlated with the extent and severity of coronary artery plaques, independent of sex and age, but the study only reflected the difference in the amount of fat distribution rather than the qualitative change of the metabolic state of the body. Moreover, high liver fat content can largely explain the metabolic state of high insulin, hyperglycaemia, hypertriglyceridemia, and elevated apolipoprotein B in visceral obesity (43). However, focusing solely on visceral fat obesity may lead to overlooking individuals with lean fatty liver. Despite this, the study by Semmler et al. demonstrated that lean nonalcoholic fatty liver disease is also associated with metabolic syndrome and increased cardiovascular disease risk (44). The liver, being a sensitive organ of ectopic fat deposition, can more accurately reflect the metabolic imbalance of lipids in the body, and SDCT examination enhances detection sensitivity. Liver multi-parameter metrics based on SDCT can aid in identifying high-risk populations for cardiovascular and cerebrovascular disorders. In clinical practice, it is recommended to utilize SDCT for longitudinal follow-up assessments to evaluate changes in hepatic fat metabolism status. Additionally, early and secondary prevention of diseases should be enhanced through guiding clinical dietary and exercise interventions to reduce the occurrence of adverse cardiovascular outcomes in the future.

There are some limitations in this study. First, histological diagnosis of hepatic steatosis was not validated. Second, because this was a single-centre cross-sectional study with a small sample size, individuals with inadequate clinical data were excluded, which may have led to a selection bias. Consequently, this may exert an influence on the interpretation and generalizability of the study findings. Third, the measurement indicators of liver SDCT were derived from a fixed-size ROI in the right lobe of the liver, which may not reflect the degree of fat deposition throughout the entire liver. Technologies based on artificial intelligence are already capable of assessing VAT, such as pericardial adipose tissue (45) and abdominal adipose tissue (46). This development will offer a new perspective for the subsequent development of artificial intelligence for liver fat quantification based on SDCT, which is also one of our upcoming research directions. Finally, we only explored the relationship between liver SDCT parameters and populations at high risk of coronary disease, and the diagnostic evidence for the prognosis of cardiovascular disease is insufficient. We will conduct a follow-up study to verify the auxiliary incremental predictive value of spectral CT for the long-term prognosis of cardiovascular disease.

Conclusions

Liver multi-parameter metrics can better reflect the changes in liver tissue components than polychromatic images metrics and are closely associated with the extent and severity of coronary plaque scores. In particular, liver quantitative indicators assessed by Zeff based on SDCT may be useful for evaluating the risk of CAD. Early identification of liver tissue components is beneficial for screening individuals at high risk of cardiovascular ailments and enabling early clinical intervention.

Acknowledgments

The authors would like to acknowledge Xiaomei Lu from CT Clinical Science, Philip Healthcare China, for the assistance with CT technology.

Funding: This work was supported by the National Natural Science Foundation of China (No. 82071920), the Key Research & Development Plan of Liaoning Province (No. 2020JH2/10300037), and the 345 Talent Project in Shengjing Hospital of China Medical University and the Outstanding Scientific Fund of Shengjing Hospital.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-53/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-53/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of Shengjing Hospital of China Medical University (No. 2022PS958K) and informed consent was taken from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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