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Skeletal muscle and BMI are essential prognostic factors for survival in colorectal cancer (CRC). However, there is a lack of understanding due to scarce studies on the continuous aspects of these variables.
This study aimed to evaluate the prognostic impact of the initial status and trajectories of muscle and BMI on overall survival (OS) and assess whether these 4 profiles within 1 year can represent the profiles 6 years later.
We analyzed 4056 newly diagnosed patients with CRC between 2010 to 2020. The volume of the muscle with 5-mm thickness at the third lumbar spine level was measured using a pretrained deep learning algorithm. The skeletal muscle volume index (SMVI) was defined as the muscle volume divided by the square of the height. The correlation between BMI status at the first, third, and sixth years of diagnosis was analyzed and assessed similarly for muscle profiles. Prognostic significances of baseline BMI and SMVI and their 1-year trajectories for OS were evaluated by restricted cubic spline analysis and survival analysis. Patients were categorized based on these 4 dimensions, and prognostic risks were predicted and demonstrated using heat maps.
Trajectories of SMVI were categorized as decreased (812/4056, 20%), steady (2014/4056, 49.7%), or increased (1230/4056, 30.3%). Similarly, BMI trajectories were categorized as decreased (792/4056, 19.5%), steady (2253/4056, 55.5%), or increased (1011/4056, 24.9%). BMI and SMVI values in the first year after diagnosis showed a statistically significant correlation with those in the third and sixth years (
Profiles within 1 year of both BMI and muscle were surrogate indicators for predicting the later profiles. Continuous trajectories of body and muscle mass are independent prognostic factors of patients with CRC. An automatic algorithm provides a unique opportunity to conduct longitudinal evaluations of body compositions. Further studies to understand the complicated natural courses of muscularity and adiposity are necessary for clinical application.
Colorectal cancer (CRC) is the third most common cancer, accounting for approximately 10% of newly diagnosed cancers and cancer-related mortality worldwide [
Given the rapid global increase in obesity in recent years, understanding the effects of obesity on cancer outcomes is important [
Previously, skeletal muscle was manually measured using computed tomography (CT) images at the level of the third lumbar spine vertebra (L3), which includes an intense localization procedure and inevitable operator errors in the manual identification of the L3 vertebrae [
To comprehend the natural course of adiposity and muscularity of patients with CRC, we continuously tracked skeletal muscle and BMI profiles over 1 year and evaluated their representativeness for longer prospective profiles. Moreover, we evaluated the prognostic effects of the trajectories of changes in skeletal muscle and BMI to identify their longitudinal effects on CRC prognosis and demonstrated predicted mortality risks for each body composition trajectory, specified by initial status and changes in BMI and muscularity. Our study highlights the continuous characteristics of muscle and BMI in OS and supports the clinical applicability of artificial intelligence–powered automated evaluation for CRC risk modification and management.
A total of 4056 patients with newly diagnosed CRC were enrolled in the Yonsei Cancer Registry Database between January 1, 2010, and September 30, 2020. Patients with abdominal CT images and BMI information up to 1 year after diagnosis were eligible. CT images within 1 year of diagnosis were obtained from the medical database of Severance Hospital. BMI was measured as the patient’s weight in kilograms divided by the square of the patient’s height in meters (kg/m2). Skeletal muscle volume index (SMVI) outliers and SMVI change ratios were detected using median absolute deviation and were excluded. Demographic factors, such as age, sex, weight, height, variables related to diagnosis, progression, the treatment of CRC, and the date of death or follow-up loss, were collected.
Using the UNet architecture–based automated skeletal muscle measurement algorithm proposed by Islam et al [
Study design. (A) The process of skeletal muscle segmentation and patient classification according to SMVI patterns. Axial CT image series were inputted (left). The blue area indicates the segmented muscle area and the volume of the muscle with 5-mm thickness was calculated (middle). Patients were classified into SMVI decreased, steady, and increased groups with the SMVI change ratio (right). (B) The process of patient classification according to 1-year BMI patterns. Lines indicate the 1-year BMI pattern of individual patients. The centers of 3 clusters by k-means clustering are presented. The 1-year BMI trajectories of the patients are demonstrated within each BMI pattern. CT: computed tomography; DICOM: Digital Imaging and Communications in Medicine; L3: third lumbar spine vertebra; SMVI: skeletal muscle volume index.
Baseline SMVI was defined as the mean SMVI within 3 months after diagnosis. SMVI change ratios were calculated as the difference between the baseline and last SMVI values. SMVI patterns were divided into 3 groups according to the SMVI change ratio: below −5% as the decreased group, over 5% as the increased group, and between −5% to 5% as the steady group (
Patient characteristics were compared between BMI pattern groups. The normality of variables was determined using the quantile-quantile plot. Parametric and nonparametric continuous variables were compared using ANOVA and Kruskal-Wallis tests, respectively. Categorical variables were compared using the chi-square test. The effect sizes for ANOVA, Kruskal-Wallis test, and chi-square test were individually calculated using partial eta squared [
The correlation between the BMI of the first, third, and sixth years after diagnosis was evaluated with Pearson correlation, among patients with available BMI data for a sufficient period. Identical correlation analysis was conducted with SMVI among 300 random patients with available CT images for a sufficient period. The association of 6-year overall mortality with baseline and change ratio of both SMVI and BMI was analyzed using restricted cubic spline analysis. The nonlinearity was assessed by Wald statistics. Patients’ 6-year OS was analyzed for survival analysis, and survival curves were plotted using the Kaplan-Meier method and compared between SMVI pattern groups using the log-rank test. Cox proportional hazard regression was conducted to predict 6-year OS. Similar survival analyses were performed for each BMI pattern group. All statistical analyses were conducted using JupyterLab (version 1.2.6; Project Jupyter) [
Three hazard ratio (HR) heat maps were designed with HRs of the survival analysis, with the
The study design was approved by the institutional review board of Severance Hospital, Seoul, South Korea (IRB 4-2020-1304). The need for informed consent was waived by the ethics committee, as this study used routinely collected log data managed anonymously at all stages, including data cleaning and statistical analyses. The study protocol was performed following the guidelines of the International Conference on Harmonization of Good Clinical Practice, the Declaration of Helsinki, and relevant legislation for observational studies.
The median follow-up time was 45.5 (range 12.0-129.5) months. The median age was 61.0 (IQR 52.0-69.0) years, and 56.7% (2299/4056) of patients were men. At the time of diagnosis, the average baseline BMI was 23.2 (SD 3.0) kg/m2. Of the 4056 patients, 4.7% (n=192) were classified as underweight, 43.9% (n=1780) as normal weight, 25.1% (n=1019) as preobese, 23.9% (n=969) as obese stage 1, and 2.4% (n=96) as obese stages 2-3. The mean SMVI at diagnosis was 20.7 (SD 4.1) cm3/m2.
Patients were classified by SMVI patterns into 3 groups: decreased (812/4056, 20%), steady (2014/4056, 49.7%), and increased (1230/4056, 30.3%) groups. Using the k-means clustering method, clustering BMI patterns into 3 groups—decreased (792/4056, 19.5%), steady (2253/4056, 55.5%), and increased (1011/4056, 24.9%) groups—was the most optimized clustering option (
The baseline characteristics of each group according to BMI patterns are summarized in
Patient characteristics according to 1-year BMI trajectory group: decreased, steady, and increased.
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1-year BMI trajectory pattern group | Effect sizeb | ||||||
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Decreased (n=792) | Steady (n=2253) | Increased (n=1011) |
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Age (years), median (IQR) | 61.0 (52.0-69.0) | 61.0 (53.0-69.0) | 60.0 (52.0-67.5) | .01 | .002 | |||
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.02 | .044 | ||||||
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Male | 474 (59.8) | 1285 (57) | 540 (53.4) |
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Female | 318 (40.2) | 968 (43) | 471 (46.6) |
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BMI (kg/m2) | 24.4 (3.1) | 23.2 (3.0) | 22.5 (2.9) | <.001 | .043 | ||
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SMVIc (cm3/m2) | 21.4 (4.1) | 20.8 (4.1) | 20 (3.8) | <.001 | .013 | ||
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<.001 | .141 | ||||||
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Underweight | 12 (1.5) | 107 (4.7) | 73 (7.2) |
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Normal | 262 (33.1) | 979 (43.5) | 539 (53.3) |
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Preobese | 206 (26) | 604 (26.8) | 209 (20.7) |
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Obesity stage 1 | 277 (35) | 515 (22.9) | 177 (17.5) |
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Obesity stages 2-3 | 35 (4.4) | 48 (2.1) | 13 (1.3) |
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<.001 | .213 | ||||||
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Decreased | 311 (39.3) | 398 (17.7) | 103 (10.2) |
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Steady | 356 (44.9) | 1219 (54.1) | 439 (43.4) |
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Increased | 125 (15.8) | 636 (28.2) | 469 (46.4) |
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BMI (kg/m2) | −9.1 (4.1) | 0.4 (2.8) | 11.0 (5.4) | <.001 | .752 | ||
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SMVI (cm3/m2) | −2.8 (9.1) | 1.4 (7.8) | 4.9 (8.7) | <.001 | .088 | ||
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<.001 | .116 | ||||||
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I | 83 (10.5) | 273 (12.1) | 26 (2.6) |
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II | 118 (14.9) | 462 (20.5) | 172 (17) |
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III | 320 (40.4) | 924 (41) | 447 (44.2) |
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IV | 271 (34.2) | 594 (26.4) | 366 (36.2) |
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Recurrence or metastasis, n (%) | 374 (47.2) | 851 (37.8) | 507 (50.1) | <.001 | .113 | |||
Death, n (%) | 228 (28.8) | 538 (23.9) | 261 (25.8) | .02 | .043 | |||
Follow-up duration (years), mean (SD)d | 3.7 (3.3) | 4.0 (4.1) | 3.8 (3.6) | <.001 | .008 | |||
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Surgery | 737 (93.1) | 2071 (91.9) | 902 (89.2) | .008 | .049 | ||
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CTxe | 686 (86.6) | 1822 (80.9) | 961 (95.1) | <.001 | .168 | ||
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RTxf | 377 (47.6) | 613 (27.2) | 256 (25.3) | <.001 | .181 |
aCategorical variables were compared using the chi-square test. Continuous variables were compared using ANOVA and Kruskal-Wallis tests.
bCramer V effect size: 0.1=small, 0.3=medium, and 0.5=large (for 2 subvariable comparisons) and 0.07=small, 0.21=medium, and 0.35=large (for more than 3 subvariable comparisons). Partial eta-squared effect size: 0.01=small, 0.06=medium, and 0.14=large. Eta-squared effect size: 0.01=small, 0.06=medium, and 0.14=large.
cSMVI: skeletal muscle volume index.
dThe follow-up duration of patients with a follow-up period longer than 6 years was considered as 6 years.
eCTx: chemotherapy.
fRTx: radiotherapy.
Among the 4056 patients, BMI data were available for 3217 patients 3 years after diagnosis and for 1318 patients 6 years after diagnosis. Among these patients, BMI in the first and third years showed a statistically significant positive correlation (
Correlation between BMI or SMVI profiles at the first year and 3 and 6 years after diagnosis. Scatter plots of patients’ BMI or SMVI profiles are presented, and results of trend line formula, R-square, and
According to restricted cubic spline analysis, baseline BMI and mortality risk exhibited a U-shaped relationship (
Change ratios of BMI demonstrated an inverse correlation with overall mortality (
The relationship of the baseline and trajectories of obesity and muscle mass with mortality using restricted cubic spline analysis. Relationship between overall mortality and (A) baseline BMI, (B) baseline SMVI, (C) BMI change ratio, and (D) SMVI change ratio.
The trajectory of the decreased SMVI group was associated with shorter OS (6-year OS rate: 63.4% in decreased SMVI vs 72.4% in steady SMVI vs 69.6% in increased SMVI;
Based on the trajectories of BMI changes affecting OS, we performed a subgroup analysis according to 3 BMI trajectories (
Kaplan-Meier curve and Cox proportional hazard regression analysis. Results for (A) total patients, (B) decreased BMI group, (C) steady BMI group, and (D) increased BMI group are presented. Kaplan-Meier curve results are shown on the left. Cyan, magenta, and yellow lines indicate decreased, steady, and increased SMVI groups, respectively. The hazard ratio and 95% CIs are shown on the right. Red and blue marked variables are statistically significant (
In consideration of baseline BMI and SMVI at the time of diagnosis, the highest mortality risk was observed in patients with high body mass (BMI >30 kg/m2) and low SMVI (HR 2.09;
Finally, we generated an HR heat map depicting the baseline and trajectories of both BMI and SMVI (
Predicted mortality risk heat map representing associations between BMI, skeletal muscle volume index (SMVI), and overall survival (OS). Patient groups of each heat map were classified by (A) baseline status classified by 5 baseline BMI and 3 baseline SMVI groups, (B) pattern groups classified by 3 BMI and 3 SMVI pattern groups, and (C) all 4 dimensions: baseline BMI and SMVI profiles and trajectories of BMI and SMVI. The predicted mortality risk of each specific patient group is described in each corresponding square, and colors represent the natural logarithm of the predicted mortality risk.
This study evaluated the characteristics of baseline BMI and SMVI and their 1-year trajectories along with the prognostic impacts of these 4 parameters on OS in patients with CRC. The BMI and SMVI profiles of the first year after diagnosis were highly correlated with the third- and sixth-year profiles, implying that these are surrogate indicators for representing subsequent body composition profiles. Moreover, our survival analysis results indicated that high muscularity positively impacted OS, whereas both depletion and excess adiposity adversely impacted survival. Alterations in adiposity and muscle mass significantly affected 6-year overall mortality. Reciprocal compensation of these 2 factors indicated that changes in BMI had a superior prognostic impact than changes in muscle.
According to our restricted cubic spline analysis, baseline SMVI did not exhibit a nonlinear relationship with mortality, whereas baseline BMI demonstrated a U-shaped relationship with mortality. In addition, high muscle mass at baseline exhibited a protective effect against survival, and extreme BMI (underweight or severe obesity) predicted lower survival. These findings suggest that muscularity at the time of diagnosis may have protective effects, whereas both adiposity depletion and excess may have disadvantages for survival. The results of the survival analysis of our study also support the independent prognostic effects of body composition profiles at the time of diagnosis. The mechanisms underscoring the impact of body composition on CRC mortality at the time of diagnosis have yet to be elucidated because completely eliminating methodological biases is challenging [
In this study, an increase in muscle mass or BMI demonstrated higher OS, highlighting the beneficial effects of increasing muscle and body mass. Survival analysis within subgroups of each BMI trajectory group showed a combined prognostic influence of muscularity and adiposity. HR results of the survival analysis conducted within steady and increased BMI groups described that survival improved in the order of increased, steady, and decreased SMVI. However, within the decreased BMI group, an increase in SMVI was associated with poor survival compared with steady SMVI, which contradicts general findings. Although several findings were statistically insignificant, this implies that the protective role of increased muscle differs regarding the characteristics of BMI changes, since the prognostic effects of changes in muscle and adiposity may reciprocally compensate. In addition, according to the survival analysis considering all 9 patterns of BMI and SMVI change, patients in the increased SMVI with decreased BMI group had statistically significant increased mortality risk. The superior decrease of adiposity that leads to decreased BMI despite the improvement of muscularity may override the protective effects of increased muscle mass.
Although BMI is known to have limitations in the direct representation of adiposity [
Previous studies support the association between physical activity and cancer prognosis. A prospective cohort study revealed that sedentary behavior is associated with cancer mortality risk and replacing sedentary time with physical activities may improve OS [
Our study has the following strengths. We analyzed a large size of patients and assessed various natural courses of patients with CRC who did not receive additional interventions besides clinical practices. To comprehend the role of adiposity and muscularity in CRC progression, understanding its alternating nature is necessary for evaluating time-dependent prognostic impacts. Therefore, longitudinal patterns of BMI and muscle were analyzed simultaneously. Given that adiposity and muscle commonly change during cancer treatments [
Our study has several limitations. We evaluated the 6-year OS as a survival outcome using 1-year trajectories of BMI and muscle mass. However, according to the correlation analysis, trajectory analysis up to 1 year was sufficient for representing the courses beyond a year. Since physical examinations and imaging studies are frequent during the first year as a part of the diagnosis or treatment process, we assumed that 1 year was sufficient for classifying body composition profiles. Prolonged longitudinal trajectorial analysis may provide additional insight into the longer-term effects of adiposity or muscle in cancer survival, thereby facilitating personalized risk assessment and management.
In conclusion, our findings demonstrated the natural courses of BMI and muscle and highlight the 1-year trajectory as a surrogate indicator for representing further progress, wherein deep learning–based automated muscle assessment enabled longitudinal analysis of muscle. Thus, this study can be ground evidence for further research regarding prognostic improvements of patients with cancer by interventions that improve body composition profiles. In addition, our results provided crucial insights into the prognostic roles of adiposity and muscle mass in CRC survival. Body composition profiles at the time of diagnosis and alterations of these parameters are independent prognostic factors of CRC survival. The combination of BMI, which is frequently gathered but crude, with muscle-related parameter, which is automatically analyzed but occasionally obtained, could be a precise risk assessment tool. For further clinical application, supporting studies evaluating the natural course of patients with CRC will be necessary and more personalized prognosis prediction methods are required. To fully understand the role of adiposity and muscularity in cancer progression, impacts on other clinical outcomes should also be studied.
The inertia result of the k-means clustering of patients’ 1-year trajectory according to cluster numbers from 1 to 10. The most efficient clustering result was 3 clusters using the elbow method, and its inertia is denoted by the red circle.
Cox proportional hazard regression result within total patients. Adjusted variables were age at diagnosis (above or below 65 years); sex; stage; primary cancer location (colon or rectum); histology (adenocarcinoma or others); recurrence or metastasis; the administration of surgery, chemotherapy, or radiotherapy; baseline BMI (underweight, normal, preobese, obesity stage 1, or obesity stages 2-3); baseline SMVI (low, normal, or high); and patterns of BMI and SMVI (decreased, steady, or increased). SMVI: skeletal muscle volume index.
Cox proportional hazard regression result within the decreased BMI group. Adjusted variables were age at diagnosis (above or below 65 years); sex; stage; primary cancer location (colon or rectum); histology (adenocarcinoma or others); recurrence or metastasis; the administration of surgery, chemotherapy, or radiotherapy; baseline BMI (underweight, normal, preobese, obesity stage 1, or obesity stages 2-3); baseline SMVI (low, normal, or high); and patterns of SMVI (decreased, steady, or increased). SMVI: skeletal muscle volume index.
Cox proportional hazard regression result within the steady BMI group. Adjusted variables were age at diagnosis (above or below 65 years); sex; stage; primary cancer location (colon or rectum); histology (adenocarcinoma or others); recurrence or metastasis; the administration of surgery, chemotherapy, or radiotherapy; baseline BMI (underweight, normal, preobese, obesity stage 1, or obesity stages 2-3); baseline SMVI (low, normal, or high); and patterns of SMVI (decreased, steady, or increased). SMVI: skeletal muscle volume index.
Cox proportional hazard regression result within the increased BMI group. Adjusted variables were age at diagnosis (above or below 65 years); sex; stage; primary cancer location (colon or rectum); histology (adenocarcinoma or others); recurrence or metastasis; the administration of surgery, chemotherapy, or radiotherapy; baseline BMI (underweight, normal, preobese, obesity stage 1, or obesity stages 2-3); baseline SMVI (low, normal, or high); and patterns of SMVI (decreased, steady, or increased). SMVI: skeletal muscle volume index.
Cox proportional hazard regression result for hazard ratio heat map. Adjusted variables were age at diagnosis (above or below 65 years); sex; stage; primary cancer location (colon or rectum); histology (adenocarcinoma or others); recurrence or metastasis; the administration of surgery, chemotherapy, or radiotherapy; baseline BMI (underweight, normal, preobese, obesity stage 1, or obesity stages 2-3); baseline SMVI (low, normal, or high); and 9 patient groups divided by three 1-year BMI trajectories (decreased, steady, or increased) and three 1-year SMVI trajectories (decreased, steady, or increased). SMVI: skeletal muscle volume index.
Survival analysis results against total patients for hazard ratio heat map. (A) Kaplan-Meier curve and survival curve comparison by log-rank test. Patient groups were classified with 1-year trajectories of BMI and SMVI and are represented in different colors. (B) Cox proportional hazard regression result for hazard ratio heat map. Adjusted variables were age at diagnosis (above or below 65 years); sex; stage; primary cancer location (colon or rectum); histology (adenocarcinoma or others); recurrence or metastasis; the administration of surgery, chemotherapy, or radiotherapy; baseline BMI (underweight, normal, preobese, obesity stage 1, or obesity stages 2-3); baseline SMVI (low, normal, or high); and 9 patient groups divided by three 1-year BMI trajectories (decreased, steady, or increased) and three 1-year SMVI trajectories (decreased, steady, or increased). SMVI: skeletal muscle volume index.
colorectal cancer
computed tomography
hazard ratio
third lumbar spine vertebra
overall survival
skeletal muscle volume index
World Health Organization
This research was supported by the Bio-Industrial Technology Development Program (20014841) funded by the Ministry of Trade, Industry & Energy (MOTIE; South Korea) and a grant from the National Research Foundation of Korea (NRF) funded by the Korean government (Ministry of Science and ICT [MSIT]; grant 2022R1A2C4001879). We also thank the Medical Illustration & Design, part of the Medical Research Support Services of Yonsei University College of Medicine, South Korea, for revising illustrations for this work. Additionally, this study was supported by the Division of Digital Health of the Yonsei University Health System, Seoul, South Korea, with regard to data curation.
Raw data for this study were generated at Severance Hospital. Derived data supporting the findings of this study are available from the corresponding author upon request.
DS and HSK contributed equally as co–first authors. DS, HSK, and YRP contributed to conceptualization. DS, HSK, JBA, and YRP contributed to data curation. DS, HSK, and YRP contributed to methodology. DS contributed to formal analysis, software, and writing—original draft. YRP and HSK contributed to writing—review and editing. YRP, HSK, and JBA contributed to supervision.
None declared.