Albuminuria and neck circumference are determinate factors of successful accurate estimation of glomerular filtration rate in high cardiovascular risk patients
Albuminuria and neck circumference are determinate factors of successful accurate estimation of glomerular filtration rate in high cardiovascular risk patients
Po-Jen Hsiao 0 1
Hung-Che Lin 0
Shih-Tai Chang 0
Jen-Te Hsu 0
Wei-Shiang Lin 0
Chang-Min Chung 0
Jung-Jung Chang 0
Kuo-Chun Hung 0
Yun-Wen Shih 0
Fu-Chi Chen 0
Fu-Kang Hu 0
Yi-Syuan Wu 0
Chi-Wen Chang 0 2
Sui-Lung Su 0 3
Chi- Ming Chu 0 3 4
0 Editor: Abelardo I Aguilera, Hospital Universitario de la Princesa , SPAIN
1 Division of Nephrology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center , Taipei, Taiwan, R.O.C , 2 Division of Nephrology, Department of Internal Medicine, Taoyuan Armed Forces General Hospital , Taiwan, R.O.C , 3 Big Data Research Center, Fu-Jen Catholic University , Taiwan, R.O.C , 4 Department of Otolaryngology±Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center , Taipei, Taiwan, R.O.C , 5 Division of Cardiology, Chang Gung Memorial Hospital-Linkou, Chang GungUniversity College of Medicine , Tao-Yuan, Taiwan, R.O.C , 6 Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center , Taipei, Taiwan, R.O.C , 7 School of Public Health, National Defense Medical Center , Taipei, Taiwan, R.O.C , 8 Department of Biomedical Engineering, National Defense Medical Center , Taipei , Taiwan, R.O.C
2 School of Nursing, College of Medicine, Chang Gung University , Taoyuan, Taiwan, R.O.C , 10 Division of Endocrinology, Department of Pediatrics, Linkou Chang Gung Memorial Hospital , Taoyuan , Taiwan, R.O.C
3 Graduate Institute of Life Sciences, National Defense Medical Center , Taipei , Taiwan, R.O.C
4 Department of Public Health, China Medical University , Taichung City , Taiwan, R.O.C
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
relations with eGFR.
We reviewed the records of patients with high cardiovascular risk from 2010 to 2011 in
Taiwan. 24-hour urine creatinine clearance was used as the standard. We utilized a decision
tree to select for variables and adopted a stepwise regression method to generate five
models. Model 1 was based on only serum creatinine and was adjusted for age and gender.
Model 2 added serum cystatin C, models 3 and 4 added albuminuria and neck
circumference, respectively. Model 5 simultaneously added both albuminuria and neck
Total 177 patients were recruited in this study. In model 1, the bias was 2.01 and its
precision was 14.04. In model 2, the bias was reduced to 1.86 with a precision of 13.48. The bias
of model 3 was 1.49 with a precision of 12.89, and the bias for model 4 was 1.74 with a
precision of 12.97. In model 5, the bias could be lower to 1.40 with a precision of 12.53.
In this study, the predicting ability of eGFR was improved after the addition of serum cystatin
C compared to serum creatinine alone. The bias was more significantly reduced by the
calculation of albuminuria. Furthermore, the model generated by combined albuminuria and
neck circumference could provide the best eGFR predictions among these five eGFR
models. Neck circumference can be investigated potentially in the further studies.
Over the past ten years, the prevalence and incidence of chronic kidney disease (CKD) and
end-stage renal disease (ESRD) have risen rapidly in all countries, leading to further
comorbiditity and mortality [1±3]. The glomerular filtration rate (GFR) is an indicator that is currently
used to assess renal function. Currently, the gold standard for the confirmation of renal
function is to inject exogenous substances such as inulin and nuclear medicinal substances into the
body and to then detect the concentrations of these substances in the blood or urine to
determine the glomerular filtration conditions . Although these methods are highly accurate, the
processes are complicated, time-consuming, and expensive. Moreover, clinically, the 24-hour
urine creatinine clearance (24 hours CCR) is often used as a standard. However, this method is
complicated and is easily affected by the degree of patient cooperation for urine collection .
Previously, the detection of renal function was often performed using methods such as
collecting a single urine sample to measure trace proteins and collecting blood samples to
measure serum creatinine levels. However, many studies have shown that determining kidney
disease on the basis of urine or serum creatinine alone tends to overestimate renal function. In
addition, serum creatinine is mainly affected by muscle mass. Thus, recently developed renal
function assessment models have been established on the basis of endogenous indicators of
serum creatinine and have been adjusted for factors that may affect creatinine, including age,
gender, body surface area, and race, to enhance detection accuracy. Many studies have found
that commonly used estimated GFR (eGFR) models, including the Modification of Diet in
Renal Disease (MDRD) and Cockcroft-Gault (CG) model, demonstrate a poor predictive abil
ity for the loss of kidney function at early stages. Moreover, particularly in Asian populations,
these models are prone to overestimate renal function, such that patients with early CKD
cannot be detected with accuracy [5,6]. Other single serum creatinine-based formula, The Chronic
Kidney Disease Epidemiology Collaboration (CKD-EPI) equation has also been reported to be
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an accurate formula compared with MDRD. Despite serum creatinine, other endogenous
substance such as cystatin C has been used more as an evaluation indicator. Serum cystatin
Cbased formula has been reported be more accurate than CKD-EPI equation [7±9]. Improved
predicting ability of eGFR by combined serum creatinine and cystatin C measurements also
has been reported [10±12].
In this clinical study, the GFR, which is reflected by 24 hours CCR corrected for body
surface area, was used as the standard to assess renal function. Albuminuria is an important
marker of estimation of kidney functions, which could help the detection of progressive CKD
at early stages . We measure the levels of albumin in urine as to assess for kidney damage,
and compare the correlations of the concentrations of the endogenous serum indicators,
creatinine and cystatin C, with changes in renal function. Additionally, neck circumference (NC) is
an anthropometric measure of obesity for subcutaneous adipose tissue distribution which has
been reported to be associated with cardiometabolic risk and CKD [14,15]. To date, the most
used method for eGFR is still MDRD model in Taiwan. However, it is unsatisfied in clinical
practice. We investigate whether albuminuria and NC is associated with renal function in high
risk CKD patients. Finally, we establish five different models to calculate the eGFR and
compare these traditional and more commonly used models in Taiwan.
Patients and data collection
Research framework. This study is a cross-sectional study, which analyzed the collected
data and established models. This study was approved by the human trial committee of the
Tri-Service General Hospital and Chiayi Chang Gung Memorial Hospital. Approval for this
study was provided by the Institutional Review Board (IRB) of the Chang Gung Memorial
Hospital (Approval No: CGMH- IRB-99-3623B).
Research subjects and data collection. The patient cases consisted of outpatients
recruited from the cardiology clinics at the Tri-Service General Hospital and Chiayi Chang
Gung Memorial Hospital from 2010±2011. The inclusion criteria included patients who were
over 20 years of age, had normal renal function or were diagnosed between stages 1 to 5 with
CKD. The exclusion criteria included patients with acute renal failure, hereditary kidney
disease, other kidney associated diseases, cancer, pregnancy, breastfeeding, long-term use of
steroids, or recent radioactive examinations. Patients who participated in the study were required
to sign a consent form after we had explained the purpose and content of the study. In this
present study, according to Magnani (1997) suggested the formulation for calculating sample
size under 95% confidence interval which is alpha error as the following:
The sample size was determined around 139 = (1.96)^2×(0.9)×(0.1) / (0.05)^2.
Outcome measures and definitions
Assessment of renal function. The GFR, which was represented by the 24 hours CCR
adjusted for body surface area (rGFR) (ml/min/1.73 m2), was used as the standard for renal
function assessment, and the urine albuminuria conditions were measured to assess for renal
injury. The 24 hours CCR model was as follows: (24-hour urine volume × urine creatinine
concentration) / (serum creatinine concentration × 1440), adjusted for a body surface area of
1.73 m2. To determine the level of albuminuria, the urinary albumin concentration was
multiplied by the urine volume to obtain the 24-hour albuminuria concentration (mg/day). The
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creatinine concentration measurement was based on the Modified Jaffe Method and was
determined using the SYNCHRON LXI 725 (Tokyo, Japan). The serum cystatin C
concentration measurement method was based on the particle-enhanced turbidimetric immunoassay
and performed using the Hitachi 7170 series analyzer (Tokyo, Japan).
Data processing. For statistical analyses, SPSS 18.0 was used. If the data did not have a
normal distribution, then the logarithmic transformation was used to normalize the data.
Spearman's correlation was used to analyze the correlations of the variables to examine the
correlations of the serum biochemical parameters with the standard rGFR and kidney damage.
The α-level was set at 0.05, and a p-value <0.05 indicated that the correlation between the vari
ables was statistically significant.
Feature selection. In this present study, we analyzed the relations with measures of renal
function among NC (which is much easier measured and less variance) and most
anthropometric and renal physiological measurements such as body mass index, circumferences of
waist and hip, age, systolic blood pressure, diastolic blood pressure, fasting blood glucose,
serum triglyceride, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total
cholesterol, creatinine, cystatin C, blood urea nitrogen, uric acid, total protein, albumin,
C-reactive protein, Glutamic Oxaloacetic Transaminase (GOT), Glutamic Pyruvic Transaminase
(GPT), total bilirubin, calcium, phosphate, sodium, and microalbuminuria. Furthermore, we
analyzed feature selection for modeling using the decision tree, including several variables
(age, creatinine, cystatin C, NC, and microalbuminemia) (S1 Fig). The model equations
included age, creatinine, cystatin C, NC, microalbuminemia, and gender for further regression
models of eGFR compared with equations of CG and MDRD.
Model establishment. A decision tree was used to analyze and determine the variables of
the model, and the basic case information, medical history, lifestyle, and blood biochemical
indicator information were used as independent variables. The rGFR was used as the
dependent variable. The results obtained using the decision tree were compared with Spearman's
correlation to determine the variables of the model. Stepwise multiple regression was
performed using the selected variables as independent variables, and the logarithmically
transformed eGFR was used as the dependent variable. Non-normal variables were also
logarithmically transformed and placed into the variables to establish the eGFR model. Finally,
the best-fit model was selected based on the R2 explanatory power of the model. The samples
of this study were then placed back into the model to make predictions. In addition, the
samples were also fitted into the MDRD and CG models to obtain the predictive values for each
model, and the reliability and validity of the models were then compared.
Study population characteristics
Basic demographic data. This study included a total of 177 outpatients who were
recruited from cardiology clinics. The basic patient demographic data are shown in Table 1.
The average age of the patients was 66.6 ± 9.6 years, and the majority of the patients were male
(70.6%). The average neck circumference was 38.7 ± 3.8 cm. The average blood pressure was
135.7 ± 13.8/77.6 ± 8.2 mm Hg.
Blood and urine biochemical parameters. The average value of the GFR standard rGFR
was 50.4 ± 19.7 ml/min/1.73 m2. After the creatinine level was fitted into the MDRD and CG
models and converted, the eGFR values were 68.6 ± 19.3 ml/min/1.73 m2 and 66.6 ± 24.1 ml/
min/1.73 m2, respectively. Compared with the standard rGFR, these two values showed a
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SD: standard deviation; BMI: body mass index; rGFR: Standard value of GFR; MDRD: Modification in Renal Disease; CG: Cockcroft-Gault
trend of overestimating the GFR. The sample average 24-hour albumin concentration was
176.1 ± 805.1 mg/day. If the cases were grouped according to the criterion of
microalbuminuria, then 118 patients (68.6%) were considered normal (Table 1).
Establishment of the renal function assessment model
The five models established in this study are shown in Table 2. We compared the eGFR calcu
lated based on the five models with the gold standard value, and also fitted the data into the
Cockcroft-Gaultc and MDRD models for comparison. Model 1 only considered serum
creatinine with an adjustment for age and sex, where the explanatory power R2 was 0.522, and the
mean predicted value was 48.35 ± 14.70 ml/min/1.73 m . Model 2 added serum cystatin C into
the model, where the explanatory power was increased to 0.551, and the mean predicted value
was 48.47 ± 14.70 ml/min/1.73 m2. In model 3, albuminuria was added, where the explanatory
power was increased to 0.622, and the mean predicted value was 48.84 ± 16.04 ml/min/1.73 m .
In addition to the original age, gender, serum creatinine, and cystatin C, model 4 also added
NC, where the explanatory power was 0.576, and the mean predicted value was 48.59 ± 14.99
PLOS ONE | https://doi.org/10.1371/journal.pone.0185693
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ml/min/1.73 m2. Model 5 added six variables into the model, where the explanatory power was
increased to 0.635, and the mean predicted value was 48.90 ± 16.28 ml/min/1.73 m .
We compared the reliability and validity of each model in predicting the GFR. The mean
rGFR was 50.33 ± 19.26 ml/min/1.73 m2, and the MDRD and Cockcroft-Gaultc models, which
were established based on serum creatinine alone, yielded mean values of 68.54 ± 19.66 ml/min/
1.73 m2 and 66.22 ± 24.01 ml/min/1.73 m2, respectively (Table 2). Biases of the two models
compared with the mean rGFR were -18.21 and -15.89 with a precision of 16.28 and 16.95,
respectively, and the both biases showed significant differences. Model 1 was established based on serum
creatinine alone, where its bias was 2.01 and its precision was 14.04. The p-value for the bias
between the two was 0.062. After the addition of serum cystatin C to model 2, the bias between
the predicted value and the rGFR was reduced to 1.86 with a precision of 13.48, indicating that
after the addition of cystatin C, the predicting ability was improved compared to the models for
creatinine alone. Furthermore, models 3 and 4 added albuminuria and NC, respectively. The bias
of model 3 was 1.49 with a precision of 12.89, and the bias for model 4 was 1.74 with a precision
of 12.97. These results suggested that the bias was more significantly reduced by the addition of
albuminuria. Model 5 simultaneously added both variables, and the bias was 1.40 with a precision
of 12.53. The correlation of serum cystatin C with rGFR was higher compared to creatinine, while
the difference in the correlation with albumin concentration between the two indicators was only
visible in cases of massive albuminuria (r = 0.785 vs. r = 0.597) (Table 3).
Relationship between serum creatinine and renal function and between
serum cystatin C and renal function
Patients with early stage CKD may simultaneously exhibit glomerular filtration abnormalities and abnormal albuminuria caused by kidney damage, but may also only display decreased
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Normal albuminuria (n = 118)
p-value coefficient p-value
<0.001 0.038 0.686
<0.001 0.015 0.878
glomerular filtration or pathological lesions in the kidneys . Several studies have shown
that serum cystatin C responds more sensitively to a reduction in GFR and that serum cystatin
C may replace creatinine as an indicator to measure renal function. In addition, the sensitivity
of the serum cystatin C response to decreased GFR is higher compared to serum creatinine
because creatinine is re-secreted by the renal tubules. However, the creatinine response in
early stages of glomerular decline is unclear, and creatinine only increases when the
deterioration of renal function enters stage 3 [4,17]. Studies on diabetic patients or healthy subjects
have found that serum cystatin C is more sensitive during the early stages of renal dysfunction
]. Compared with serum creatinine, some studies also recommend that serum cystatin
Cbased eGFR had good clinical utility in specific populations [19±22]. However, other studies
have indicated that although serum cystatin C demonstrates a highly sensitive response to
decreased GFR at early stages of impaired renal function, the correlation of these
concentration changes with renal function is decreased when renal function deteriorates in the late
stages, whereas the sensitivity of serum creatinine increases at late stages .
Albuminuria is an important marker of estimation of kidney functions, which can help the
detection of progressive CKD at early stages . Therefore, our study further divided the
patients into different groups on the basis of the degree of kidney damage, and investigated the
relationships between the indicators and rGFR, as well as that between the indicators and
albumin concentration. The correlation of serum cystatin C with rGFR was obvious higher
compared to creatinine. The difference in the correlation with albumin concentration between the
two indicators was observed in cases of massive albuminuria (Table 3). These findings
suggested that the rGFR conditions as determined by serum cystatin C might also reflect
conditions of albuminuria. The changes in the concentration of serum cystatin C may also reflect
the GFR condition. Previous study demonstrated that serum cystatin C and GFR were not
significantly correlated in patients with normal levels of albuminuria, suggesting the correlation
between serum cystatin C and renal function was due to an interaction between cystatin C and
albuminuria, rather than a reflection of a decline in GFR [
Model establishment and comparison
Previous assessment of renal function is often determined based on urine or blood creatinine
values alone. However, because creatinine is a substance produced in muscle metabolism,
individual differences may affect the concentration of creatinine, and it may not be appropriate to
use this indicator alone to determine renal function. However, the Cockcroft-Gault and
MDRD models, which are commonly used internationally, were established solely based on
serum creatinine . In contrast, this study attempted to add serum cystatin C, albuminuria,
and NC into the eGFR model, to improve the ability to predict renal function.
This study established five eGFR models. Model 1 was based on only serum creatinine and was adjusted for age and gender. A comparison of model 1 and the Cockcroft-Gault and
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MDRD models with rGFR revealed that the Cockcroft-Gault and MDRD models had a
relatively large bias compared with rGFR, which was consistent with other studies. The level of
creatinine may also differ according to race, but the Cockcroft-Gault model does not adjust for
race and the MDRD model, only distinguishing between blacks and whites. Thus, many
studies have shown that these models demonstrate a poor predictive ability for early stages of
kidney disease. Furthermore, particularly for Asians, the extent of the underestimate is more
robust [4,6]. Model 2 added serum cystatin C, where the explanatory power showed a slight
increase compared to model 1. Furthermore, the bias between the predictive value of eGFR
and rGFR decreased from 2.01 to 1.86, and the precision also showed a slight reduction. These
findings suggested that after adding serum cystatin C, the bias of the predicted value compared
to rGFR will decrease. In addition, models 3 to 5 sequentially added albuminuria and NC. The
bias had decreased significantly after adding albuminuria, and the explanatory power also
increased to 0.62. Although the explanatory power had only increased to 0.58 after the addition
of NC, the accuracy rates within 50% and 70% were increased to 63.8% and 80.5%,
respectively, which was more accurate compared to the other models. NC is an easy associated
method for metabolic syndrome and insulin resistance and reported to be a powerful indicator
of atherosclerotic lipid abnormalities and their risk factors recently [24±26]. In our study, the
correlations of NC with rGFR (ml/min/1.73m2) and microalbuminuria (mg/day) were 0.382
(p<0.001) and 0.304 (p<0.001), respectively (S1 Table). Moreover, the correlations of NC
with body mass index (BMI) (kg/m2), waist circumference (cm), and hip circumference (cm)
were 0.566 (p<0.001), 0.515 (p<0.001), and 0.626 (p<0.001), respectively (S2 Table). The
analysis revealed that NC was not only much easier measured and less variance, but also a
good indicator for renal function and high correlations with BMI (kg/m2), waist circumference
(cm), and hip circumference (cm).
Other studies focusing on model adjustment found that models established based on serum
creatinine alone often demonstrated less stable bias, lower precision and lower validity, and
the 30% accuracy was approximately 53% to 80%. After the inclusion of serum cystatin C, the
stability increased to approximately 87%, and the explanatory power was between 70% to 95%
[11,12,27]. However, the 30% accuracy obtained in our study was approximately within the
range of 31.5% to 43.6%, and 50% and 70% accuracy was within the range of 53.7% to 63.8%
and 77.2% to 80.5%, respectively, with an explanatory power range from 0.52% to 0.64%, which
was slightly lower compared to other studies. This might be due to the different measurement
methods for the gold standard values used to assess renal function. These studies often used the
clearance of nuclear medicinal substances to detect renal function, and their detection stability
and accuracy were more accurate than the GFR estimated-based method on 24 hours CCR.
This study has several limitations. First, a new equation always shows best performance in
the development big data-set. The sample size in this study is relatively small and the study is
done only on Taiwan patients. We do not know whether our equations could also be applied
to patients from other ethnic backgrounds. This is the major limitation. Use a validation
dataset to compare a new equation with previous one may be investigated in the future. Second,
this study utilized the renal function gold standard of 24 hours CCR, which is one of the gold
standards used in clinical practice to assess renal function. This method requires the collection
of 24-hour urine samples from individual patients. Although it is not invasive, it is very time
consuming, and the accuracy is dependent on the cooperation of individual patients. For
example, patients might miss the collection during the process of urine collection or use
incorrect storage methods. These factors are likely to result in an incorrect assessment of renal
function. Thus, when collecting data for this study, we printed specific 24-hour urine collection
procedures and precautions to reduce the incidence of bias and to improve detection accuracy.
Furthermore, 24 hours CCR is usually greater than GFR by tubular secretion of creatinine.
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This may be another limitation in this study. Finally, this is a cross-sectional study and do not
infer the causal relationships between the indicators and disease. CKD staging also requires
further verification by a long-term follow-up to determine whether the decline in renal
function continued for over three months and whether the abnormal conditions of albuminuria
continued to occur within one week.
Our study results demonstrated the predicting ability of eGFR got improvement after the addi
tion of serum cystatin C compared to the traditional models for serum creatinine alone. And
the bias could be more significantly reduced by the calculation of albuminuria. The equations
combined albuminuria and NC could help provide more accurate eGFR predictions.
Anthropometric measures of obesity for subcutaneous adipose tissue distribution, such as NC can
also be investigated more in the future.
S1 Fig. Decision tree flow of this study.
S1 Table. Correlation of clinical and biochemical variables with rGFR and
S2 Table. Correlation of clinical and biochemical variables with neck circumference.
Conceptualization: Po-Jen Hsiao, Hung-Che Lin, Shih-Tai Chang, Wei-Shiang Lin, Chang
Min Chung, Yun-Wen Shih, Fu-Chi Chen, Fu-Kang Hu, Yi-Syuan Wu, Chi-Wen Chang,
Data curation: Po-Jen Hsiao, Hung-Che Lin, Shih-Tai Chang, Jen-Te Hsu, Wei-Shiang Lin,
Chang-Min Chung, Jung-Jung Chang, Yun-Wen Shih, Fu-Chi Chen, Fu-Kang Hu, Yi
Syuan Wu, Chi-Wen Chang, Chi-Ming Chu.
Formal analysis: Po-Jen Hsiao, Jen-Te Hsu, Wei-Shiang Lin, Jung-Jung Chang, Fu-Chi Chen,
Sui-Lung Su, Chi-Ming Chu.
Investigation: Po-Jen Hsiao, Shih-Tai Chang, Jen-Te Hsu, Wei-Shiang Lin, Chang-Min
Chung, Kuo-Chun Hung, Yun-Wen Shih, Fu-Kang Hu, Yi-Syuan Wu, Chi-Wen Chang,
Methodology: Po-Jen Hsiao, Shih-Tai Chang, Jung-Jung Chang, Yun-Wen Shih, Fu-Kang
Hu, Yi-Syuan Wu, Sui-Lung Su, Chi-Ming Chu.
Project administration: Shih-Tai Chang, Yi-Syuan Wu, Chi-Ming Chu.
Resources: Hung-Che Lin, Shih-Tai Chang, Jen-Te Hsu, Wei-Shiang Lin, Chang-Min Chung,
Jung-Jung Chang, Kuo-Chun Hung, Sui-Lung Su.
Software: Hung-Che Lin, Chang-Min Chung, Yun-Wen Shih, Fu-Chi Chen, Sui-Lung Su,
Supervision: Po-Jen Hsiao, Yun-Wen Shih, Sui-Lung Su, Chi-Ming Chu.
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Validation: Po-Jen Hsiao, Jen-Te Hsu, Kuo-Chun Hung, Chi-Wen Chang, Sui-Lung Su, Chi
Visualization: Chi-Wen Chang, Sui-Lung Su, Chi-Ming Chu.
Writing ± original draft: Po-Jen Hsiao, Hung-Che Lin.
Writing ± review & editing: Po-Jen Hsiao, Chi-Ming Chu.
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