Longitudinal Body Composition Changes in Diffuse Large B-cell Lymphoma Survivors: A Retrospective Cohort Study of United States Veterans
Methods
Study Cohort
A retrospective cohort of patients with a new diagnosis of DLBCL between January 1, 2001, and September 30, 2008, was assembled from the Veteran’s Health Administration Central Cancer Registry (VACCR) based on the InterLymph classification system (International Classification of Diseases [ICD]-O3 codes 9680/3 and 9684/3 for DLBCL) (17). Data were obtained from 50 treatment sites (see Supplementary Table 1, available online, for full list). Patients were excluded for the following reasons: central nervous system involvement; positive for human immunodeficiency virus; primary cutaneous DLBCL; inadequate histologic confirmation; treatment with regimens other than CHOP +/− R, no treatment, or treatment outside of the VHA; and death within one year of last treatment.
Only patients with a baseline computed tomography (CT) scan and at least one post-treatment scan were included. CT scan at treatment completion was defined as a scan performed within 90 days after the date of last treatment. CT scan at 24 months after treatment completion was defined as a scan performed between 12 to 36 months after the date of last treatment. The study was approved by the Veterans Affairs St. Louis Health Care System and Washington University institutional review boards prior to cohort assembly. Written informed consent was not required.
Clinical Data Collection
Data on histologic diagnosis, date of birth, date of diagnosis, race, sex, disease stage, and the presence of systemic B-symptoms (fever 100.4 °F, weight loss > 10% of body weight in 6 months, and night sweats) was provided by VACCR. Patient records were also linked to additional VHA administrative datasets to obtain vital sign data, including height and weight, ICD-9 codes for comorbid conditions, and date of death. Focused data abstraction was performed using the VHA Compensation and Pension Records Interchange software system to collect data on lactate dehydrogenase (LDH), chemotherapy doses, and dates of administration. CT scans were accessed through the VistA Imaging System’s Advanced Web Image Viewer (AWIV).
Body Composition Analysis
An axial image at the third lumbar vertebral level (L3) was identified (18) and thresholded based on standard Hounsfield unit (HU) ranges for skeletal muscle (−29 to + 150), subcutaneous fat (−190 to −30), and visceral fat (−150 to −50). Images were copied into the National Institute of Health’s ImageJ Program, and skeletal muscle area, subcutaneous adipose tissue area, and visceral adipose tissue area were computed for each image in cm (Figure 1). Measurements were normalized to patient’s height and expressed as lumbar skeletal muscle index (cm/m), lumbar subcutaneous adipose tissue index (cm/m), and lumbar visceral adipose tissue index (cm/m). Sarcopenia was defined as lumbar skeletal muscle index smaller than 53 cm/m in men and smaller than 41 cm/m in women, based on previously published values (19). Total body fat mass was estimated from adipose tissue cross-sectional areas as described in Mourtzakis et al. (18): whole-body fat mass (kg) = 0.042 × (fat tissue at L3 using CT [cm]) + 11.2 (r = 0.88, P < .001, standard error of estimate = 0.80 kg). This was derived from assessing body composition of cancer patients across a range of body mass index (BMI) values and is similar to the equation reported by Shen et al. for healthy adults (20).
Example of computed tomography (CT) muscle area assessment. A) Example of abdominal CT image at the third lumbar vertebral level. B) CT image is thresholded to −29 to 150 HU. CT numbers above this range are shown as white pixels, and CT numbers below this range are shown as black pixels. C) Thresholded CT image is copied into ImageJ, and black and white values are removed, leaving only areas corresponding to skeletal muscle density. Skeletal muscle is manually selected, and area is calculated in cm. CT = computed tomography; HU = Hounsfield unit.
Other Measurements and Definitions
Weight at treatment initiation was defined as weight measured within two weeks of first treatment date. Weights at treatment completion were obtained within 90 days after the date of last treatment. Height consistently recorded at any time in the clinical history was considered accurate. BMI was calculated as weight measured in kilograms divided by the square of height measured in meters (kg/m) and was categorized in accordance with World Health Organization guidelines (21).
The total number of comorbidities was calculated using ICD-9 codes for comorbid conditions present at the time of diagnosis. Age at diagnosis was dichotomized to older than age 60 years and 60 years or younger for logistic regression analysis, in concordance with the International Prognostic Index for aggressive non-Hodgkin’s lymphoma (22). LDH was dichotomized as elevated or not elevated at time of diagnosis based on local reference ranges.
Statistical Analyses
Repeated measures analysis of variance (ANOVA) evaluated changes in body composition parameters across the three visits. Subsequent pairwise comparisons of changes in body composition parameters between visits were performed with paired t tests using Bonferroni adjustment for multiple comparisons. Univariate logistic regression explored the variables associated with sarcopenia or visceral fat gain larger than 75 cm at 24 months; variables with P values of less than 0.20 were subsequently entered into a multivariable logistic regression model. A two-tailed α significance level of .05 was considered statistically significant. All statistical analyses were performed using IBM SPSS version 20.
Study Cohort
A retrospective cohort of patients with a new diagnosis of DLBCL between January 1, 2001, and September 30, 2008, was assembled from the Veteran’s Health Administration Central Cancer Registry (VACCR) based on the InterLymph classification system (International Classification of Diseases [ICD]-O3 codes 9680/3 and 9684/3 for DLBCL) (17). Data were obtained from 50 treatment sites (see Supplementary Table 1, available online, for full list). Patients were excluded for the following reasons: central nervous system involvement; positive for human immunodeficiency virus; primary cutaneous DLBCL; inadequate histologic confirmation; treatment with regimens other than CHOP +/− R, no treatment, or treatment outside of the VHA; and death within one year of last treatment.
Only patients with a baseline computed tomography (CT) scan and at least one post-treatment scan were included. CT scan at treatment completion was defined as a scan performed within 90 days after the date of last treatment. CT scan at 24 months after treatment completion was defined as a scan performed between 12 to 36 months after the date of last treatment. The study was approved by the Veterans Affairs St. Louis Health Care System and Washington University institutional review boards prior to cohort assembly. Written informed consent was not required.
Clinical Data Collection
Data on histologic diagnosis, date of birth, date of diagnosis, race, sex, disease stage, and the presence of systemic B-symptoms (fever 100.4 °F, weight loss > 10% of body weight in 6 months, and night sweats) was provided by VACCR. Patient records were also linked to additional VHA administrative datasets to obtain vital sign data, including height and weight, ICD-9 codes for comorbid conditions, and date of death. Focused data abstraction was performed using the VHA Compensation and Pension Records Interchange software system to collect data on lactate dehydrogenase (LDH), chemotherapy doses, and dates of administration. CT scans were accessed through the VistA Imaging System’s Advanced Web Image Viewer (AWIV).
Body Composition Analysis
An axial image at the third lumbar vertebral level (L3) was identified (18) and thresholded based on standard Hounsfield unit (HU) ranges for skeletal muscle (−29 to + 150), subcutaneous fat (−190 to −30), and visceral fat (−150 to −50). Images were copied into the National Institute of Health’s ImageJ Program, and skeletal muscle area, subcutaneous adipose tissue area, and visceral adipose tissue area were computed for each image in cm (Figure 1). Measurements were normalized to patient’s height and expressed as lumbar skeletal muscle index (cm/m), lumbar subcutaneous adipose tissue index (cm/m), and lumbar visceral adipose tissue index (cm/m). Sarcopenia was defined as lumbar skeletal muscle index smaller than 53 cm/m in men and smaller than 41 cm/m in women, based on previously published values (19). Total body fat mass was estimated from adipose tissue cross-sectional areas as described in Mourtzakis et al. (18): whole-body fat mass (kg) = 0.042 × (fat tissue at L3 using CT [cm]) + 11.2 (r = 0.88, P < .001, standard error of estimate = 0.80 kg). This was derived from assessing body composition of cancer patients across a range of body mass index (BMI) values and is similar to the equation reported by Shen et al. for healthy adults (20).
Example of computed tomography (CT) muscle area assessment. A) Example of abdominal CT image at the third lumbar vertebral level. B) CT image is thresholded to −29 to 150 HU. CT numbers above this range are shown as white pixels, and CT numbers below this range are shown as black pixels. C) Thresholded CT image is copied into ImageJ, and black and white values are removed, leaving only areas corresponding to skeletal muscle density. Skeletal muscle is manually selected, and area is calculated in cm. CT = computed tomography; HU = Hounsfield unit.
Other Measurements and Definitions
Weight at treatment initiation was defined as weight measured within two weeks of first treatment date. Weights at treatment completion were obtained within 90 days after the date of last treatment. Height consistently recorded at any time in the clinical history was considered accurate. BMI was calculated as weight measured in kilograms divided by the square of height measured in meters (kg/m) and was categorized in accordance with World Health Organization guidelines (21).
The total number of comorbidities was calculated using ICD-9 codes for comorbid conditions present at the time of diagnosis. Age at diagnosis was dichotomized to older than age 60 years and 60 years or younger for logistic regression analysis, in concordance with the International Prognostic Index for aggressive non-Hodgkin’s lymphoma (22). LDH was dichotomized as elevated or not elevated at time of diagnosis based on local reference ranges.
Statistical Analyses
Repeated measures analysis of variance (ANOVA) evaluated changes in body composition parameters across the three visits. Subsequent pairwise comparisons of changes in body composition parameters between visits were performed with paired t tests using Bonferroni adjustment for multiple comparisons. Univariate logistic regression explored the variables associated with sarcopenia or visceral fat gain larger than 75 cm at 24 months; variables with P values of less than 0.20 were subsequently entered into a multivariable logistic regression model. A two-tailed α significance level of .05 was considered statistically significant. All statistical analyses were performed using IBM SPSS version 20.
Results
Demographics
Of 1445 DLBCL patients initially identified, 605 patients remained after applying exclusion criteria (Figure 2). Of these, 263 did not have post-treatment scans available for analysis, leaving 342 patients with baseline and at least one post-treatment scan available for analysis and 191 patients with scans at all three time points.
STROBE Diagram. ANOVA = analysis of variance; CHOP +/− R = cyclophosphamide, doxorubicin, vincristine, and prednisone, with or without rituximab; CNS = central nervous system; CT = computed tomography; OSH = outside hospital.
Baseline characteristics of the study cohort are listed in Table 1. Among patients with at least one post-treatment scan available for analysis, the mean patient age was 63.4 years (SD = 11.2 years). Nearly all the patients were men (96.8%) and Caucasian (87.1%). B-symptoms were noted at diagnosis in 52.6% of patients, lactate dehydrogenase was elevated in 51.5% of patients, and 57.0% had stage III/IV disease. The median number of comorbidities present at diagnosis was 1.0. Mean lumbar skeletal muscle index was 55.6 cm/m, and 40.9% of patients were classified as sarcopenic at baseline.
Table 1.
Clinical and demographic characteristics of US veterans diagnosed with DLBCL who had CT scans at baseline and at least one post-treatment scan*
| Demographics and clinical characteristics | Total (n = 342) |
|---|---|
| Age, mean (SD), y | 63.4 (11.2) |
| Male sex, No. (%) | 331 (96.8) |
| Race, No. (%) | |
| White | 298 (87.1) |
| Black | 43 (12.6) |
| Other | 1 (0.3) |
| No. of comorbidities, median (IQR) | 1.0 (2.0) |
| Stage, No. (%) | |
| Stage I/II | 145 (42.4) |
| Stage III/IV | 195 (57.0) |
| Unknown | 2 (0.6) |
| LDH, No. (%) | |
| Elevated | 176 (51.5) |
| Not Elevated | 145 (42.4) |
| Unknown | 21 (6.1) |
| B-symptoms, No. (%) | |
| Yes | 180 (52.6) |
| No | 160 (46.8) |
| Unknown | 2 (0.6) |
| Type of treatment, No. (%) | |
| CHOP | 31 (9.1) |
| R-CHOP | 311 (90.9) |
| Year of diagnosis, median | 2005 |
| BMI category at diagnosis, No. (%) | |
| <18.5 kg/m2 | 4 (1.2) |
| 18.5 to < 25 kg/m2 | 90 (26.3) |
| 25 to < 30 kg/m2 | 128 (37.4) |
| ≥30 kg/m2 | 104 (30.4) |
| Unknown kg/m2 | 16 (4.7) |
| Lumbar skeletal muscle index, mean (SD), cm/m2 | 55.6 (10.2) |
| Lumbar subcutaneous adipose tissue index, mean (SD), cm/m2 | 62.9 (30.4) |
| Lumbar visceral adipose tissue index, mean (SD), cm/m2 | 62.5 (37.8) |
To address concerns of selection bias, patients with at least one post-treatment scan (n = 342), patients with scans at all three time points (n = 191), and the overall assessed cohort (n = 605) were compared. There were no statistically significant differences in baseline characteristics between the three groups (Supplementary Table 2, available online).
Body Composition Trends
Repeated measures ANOVA comparing patients with scans at baseline, immediately after treatment completion, and 24 months after treatment completion (n = 191) demonstrated that there were changes in muscle area across the three visits (P < .001) (Figure 3A). Skeletal muscle area decreased from baseline by 2.8% at treatment completion (168.8 cm vs 173.6 cm, a reduction of −4.8 cm; 95% confidence interval [CI] = −8.1 cm to −1.5 cm) but returned to baseline levels by 24 months after treatment completion (176.1 cm vs 173.6 cm, an increase of 2.5 cm; 95% CI = −0.9 cm to 5.9 cm).
Mean changes in body composition from baseline to 24 months after treatment completion. A) Mean changes in skeletal muscle area. B) Mean changes in subcutaneous fat area. C) Mean changes in visceral fat area. Error bars represent 95% confidence intervals.
There were also changes in subcutaneous adipose tissue area (P < .001) and visceral adipose tissue area over time (P < .001) (Figure 3, B and C). Subcutaneous adipose tissue area increased from baseline by 6.5% (95% CI = 2.6% to 10.5%) at treatment completion (197.3 cm vs 185.2 cm, an increase of 12.1 cm; 95% CI = 4.8 cm to 19.5 cm) and by 21.4% (95% CI = 15.7% to 27.2%) at 24 months after treatment completion (224.8 cm vs 185.2 cm, an increase of 39.6 cm; 95% CI = 29.1 cm to 50.3 cm). This corresponds to a mean increase of 1.7 kg in subcutaneous adipose tissue from baseline by 24 months after treatment. Visceral adipose tissue area increased from baseline by 4.5% (95% CI = −0.9% to 9.9%) at treatment completion (196.6 cm vs 188.1 cm, an increase of 8.5 cm; 95% CI = −1.7 cm to 18.7 cm) and by 21.6% (95% CI = 14.8% to 28.4%) at 24 months after treatment completion (228.7 cm vs 188.1 cm, an increase of 40.6 cm; 95% CI = 27.8 cm to 53.5 cm). This corresponds to a mean increase of 1.7 kg in visceral adipose tissue from baseline by 24 months after treatment.
Variables Associated with Long-Term Development of Sarcopenia
At 24 months after treatment completion, 37.9% of patients were classified as sarcopenic. Of these patients, 79.3% were sarcopenic prior to treatment while 20.7% had newly identified sarcopenia. To identify variables associated with the long-term development or maintenance of sarcopenia, patients with both baseline and 24-month post-treatment scans were analyzed (n = 293). An exploratory univariate logistic regression analysis was initially performed, and variables with P values of less than .20 were entered into a multivariable model (Table 2). In the multivariable model, older than age 60 years (odds ratio [OR] = 2.93, 95% CI = 1.46 to 5.88), weight loss greater than 5% during treatment (OR = 2.40, 95% CI = 1.12 to 5.14), and sarcopenia at baseline (OR = 17.21, 95% CI = 8.48 to 34.94) were statistically significantly associated with sarcopenia at 24 months after treatment completion, compared with age 60 years or younger, weight loss of 5% or less or weight gain during treatment, and no sarcopenia at baseline, respectively. Black race (OR = 0.29, 95% CI = 0.09 to 0.94) was protective compared with white race.
Table 2.
Univariate and multivariable logistic regression analysis of factors associated with long-term sarcopenia
| Characteristic | Univariate regression analysis | Multivariable regression analysis | ||
|---|---|---|---|---|
| OR (95% CI) | P* | OR (95% CI) | P* | |
| Age > 60 y | 3.09 (1.86 to 5.12) | <.001 | 2.93 (1.46 to 5.88) | .002 |
| Race | ||||
| White | Referent | – | Referent | – |
| Black | 0.37 (0.16 to 0.84) | .02 | 0.29 (0.09 to 0.94) | .04 |
| CHF, yes | 1.07 (0.61 to 1.87) | .82 | - | |
| COPD, yes | 1.78 (1.07 to 2.95) | .03 | 0.94 (0.46 to 1.89) | .85 |
| Diabetes, yes | 0.58 (0.35 to 0.97) | .04 | 0.76 (0.37 to 1.61) | .48 |
| No. of comorbidities | 1.09 (0.92 to 1.29) | .32 | - | |
| Stage III/IV | 1.72 (1.06 to 2.79) | .03 | 1.27 (0.65 to 2.47) | .48 |
| B-symptoms, yes | 0.95 (0.59 to 1.52) | .83 | - | |
| LDH, elevated | 1.13 (0.69 to 1.85) | .62 | - | |
| Weight change during treatment | ||||
| Loss ≤ 5% or gain | Referent | – | Referent | – |
| Loss > 5% | 1.47 (0.88 to 2.46) | .15 | 2.40 (1.12 to 5.14) | .02 |
| Baseline sarcopenia | 19.48 (10.65 to 35.60) | <.001 | 17.21 (8.48 to 34.94) | <.001 |
| Baseline BMI category | ||||
| <25 | Referent | – | Referent | – |
| 25 to < 30 | 0.31 (0.17 to 0.56) | <.001 | 0.52 (0.24 to 1.16) | .11 |
| ≥30 | 0.18 (0.09 to 0.35) | <.001 | 0.46 (0.18 to 1.18) | .11 |
Variables Associated With Long-Term Visceral Fat Gain
At 24 months, 25% of patients gained more than 75 cm in visceral fat area from baseline, corresponding to a 3.2 kg increase in visceral fat mass. To identify risk factors for visceral fat area increase of more than 75 cm, patients with both baseline and 24 month post-treatment scans were analyzed (n = 293). An exploratory univariate logistic regression analysis was initially performed, and variables with P values of less than .20 were entered into a multivariable model (Table 3). In the multivariable model, only weight gain greater than 5% during chemotherapy was statistically significantly associated with more than 75 cm visceral fat area gain from baseline (OR = 4.60, 95% CI = 2.42 to 8.74), compared with weight gain of 5% or less or weight loss.
Table 3.
Univariate and multivariable logistic regression analysis of factors associated with long-term visceral fat area gain > 75 cm2
| Characteristic | Univariate regression analysis | Multivariable regression analysis | ||
|---|---|---|---|---|
| OR (95% CI) | P* | OR (95% CI) | P* | |
| Age > 60 y | 0.95 (0.57 to 1.61) | .86 | - | |
| Race | ||||
| White | Referent | – | - | |
| Black | 0.96 (0.44 to 2.07) | .91 | - | |
| CHF, yes | 1.04 (0.56 to 1.93) | .89 | - | |
| COPD, yes | 1.38 (0.80 to 2.39) | .25 | - | |
| Diabetes, yes | 1.03 (0.60 to 1.78) | .92 | - | |
| No. of comorbidities | 0.94 (0.78 to 1.14) | .94 | - | |
| Stage III/IV | 2.56 (1.45 to 4.46) | .001 | 1.86 (0.98 to 3.52) | .06 |
| B-symptoms, yes | 2.26 (1.31 to 3.91) | .004 | 1.57 (0.85 to 2.90) | .15 |
| LDH, elevated | 1.72 (0.99 to 2.98) | .02 | 1.10 (0.60 to 2.04) | .76 |
| Weight change during treatment | ||||
| Gain ≤ 5% or loss | Referent | – | - | |
| Gain > 5% | 5.13 (2.82 to 9.34) | <.001 | 4.60 (2.42 to 8.74) | <.001 |
| Baseline sarcopenia | 1.27 (0.75 to 2.14) | .37 | - | |
| Baseline BMI category | ||||
| <25 | Referent | – | - | |
| 25 to < 30 | 0.71 (0.38 to 1.32) | .28 | - | |
| ≥30 | 0.66 (0.34 to 1.28) | .22 | - | |
Demographics
Of 1445 DLBCL patients initially identified, 605 patients remained after applying exclusion criteria (Figure 2). Of these, 263 did not have post-treatment scans available for analysis, leaving 342 patients with baseline and at least one post-treatment scan available for analysis and 191 patients with scans at all three time points.
STROBE Diagram. ANOVA = analysis of variance; CHOP +/− R = cyclophosphamide, doxorubicin, vincristine, and prednisone, with or without rituximab; CNS = central nervous system; CT = computed tomography; OSH = outside hospital.
Baseline characteristics of the study cohort are listed in Table 1. Among patients with at least one post-treatment scan available for analysis, the mean patient age was 63.4 years (SD = 11.2 years). Nearly all the patients were men (96.8%) and Caucasian (87.1%). B-symptoms were noted at diagnosis in 52.6% of patients, lactate dehydrogenase was elevated in 51.5% of patients, and 57.0% had stage III/IV disease. The median number of comorbidities present at diagnosis was 1.0. Mean lumbar skeletal muscle index was 55.6 cm/m, and 40.9% of patients were classified as sarcopenic at baseline.
Table 1.
Clinical and demographic characteristics of US veterans diagnosed with DLBCL who had CT scans at baseline and at least one post-treatment scan*
| Demographics and clinical characteristics | Total (n = 342) |
|---|---|
| Age, mean (SD), y | 63.4 (11.2) |
| Male sex, No. (%) | 331 (96.8) |
| Race, No. (%) | |
| White | 298 (87.1) |
| Black | 43 (12.6) |
| Other | 1 (0.3) |
| No. of comorbidities, median (IQR) | 1.0 (2.0) |
| Stage, No. (%) | |
| Stage I/II | 145 (42.4) |
| Stage III/IV | 195 (57.0) |
| Unknown | 2 (0.6) |
| LDH, No. (%) | |
| Elevated | 176 (51.5) |
| Not Elevated | 145 (42.4) |
| Unknown | 21 (6.1) |
| B-symptoms, No. (%) | |
| Yes | 180 (52.6) |
| No | 160 (46.8) |
| Unknown | 2 (0.6) |
| Type of treatment, No. (%) | |
| CHOP | 31 (9.1) |
| R-CHOP | 311 (90.9) |
| Year of diagnosis, median | 2005 |
| BMI category at diagnosis, No. (%) | |
| <18.5 kg/m2 | 4 (1.2) |
| 18.5 to < 25 kg/m2 | 90 (26.3) |
| 25 to < 30 kg/m2 | 128 (37.4) |
| ≥30 kg/m2 | 104 (30.4) |
| Unknown kg/m2 | 16 (4.7) |
| Lumbar skeletal muscle index, mean (SD), cm/m2 | 55.6 (10.2) |
| Lumbar subcutaneous adipose tissue index, mean (SD), cm/m2 | 62.9 (30.4) |
| Lumbar visceral adipose tissue index, mean (SD), cm/m2 | 62.5 (37.8) |
To address concerns of selection bias, patients with at least one post-treatment scan (n = 342), patients with scans at all three time points (n = 191), and the overall assessed cohort (n = 605) were compared. There were no statistically significant differences in baseline characteristics between the three groups (Supplementary Table 2, available online).
Body Composition Trends
Repeated measures ANOVA comparing patients with scans at baseline, immediately after treatment completion, and 24 months after treatment completion (n = 191) demonstrated that there were changes in muscle area across the three visits (P < .001) (Figure 3A). Skeletal muscle area decreased from baseline by 2.8% at treatment completion (168.8 cm vs 173.6 cm, a reduction of −4.8 cm; 95% confidence interval [CI] = −8.1 cm to −1.5 cm) but returned to baseline levels by 24 months after treatment completion (176.1 cm vs 173.6 cm, an increase of 2.5 cm; 95% CI = −0.9 cm to 5.9 cm).
Mean changes in body composition from baseline to 24 months after treatment completion. A) Mean changes in skeletal muscle area. B) Mean changes in subcutaneous fat area. C) Mean changes in visceral fat area. Error bars represent 95% confidence intervals.
There were also changes in subcutaneous adipose tissue area (P < .001) and visceral adipose tissue area over time (P < .001) (Figure 3, B and C). Subcutaneous adipose tissue area increased from baseline by 6.5% (95% CI = 2.6% to 10.5%) at treatment completion (197.3 cm vs 185.2 cm, an increase of 12.1 cm; 95% CI = 4.8 cm to 19.5 cm) and by 21.4% (95% CI = 15.7% to 27.2%) at 24 months after treatment completion (224.8 cm vs 185.2 cm, an increase of 39.6 cm; 95% CI = 29.1 cm to 50.3 cm). This corresponds to a mean increase of 1.7 kg in subcutaneous adipose tissue from baseline by 24 months after treatment. Visceral adipose tissue area increased from baseline by 4.5% (95% CI = −0.9% to 9.9%) at treatment completion (196.6 cm vs 188.1 cm, an increase of 8.5 cm; 95% CI = −1.7 cm to 18.7 cm) and by 21.6% (95% CI = 14.8% to 28.4%) at 24 months after treatment completion (228.7 cm vs 188.1 cm, an increase of 40.6 cm; 95% CI = 27.8 cm to 53.5 cm). This corresponds to a mean increase of 1.7 kg in visceral adipose tissue from baseline by 24 months after treatment.
Variables Associated with Long-Term Development of Sarcopenia
At 24 months after treatment completion, 37.9% of patients were classified as sarcopenic. Of these patients, 79.3% were sarcopenic prior to treatment while 20.7% had newly identified sarcopenia. To identify variables associated with the long-term development or maintenance of sarcopenia, patients with both baseline and 24-month post-treatment scans were analyzed (n = 293). An exploratory univariate logistic regression analysis was initially performed, and variables with P values of less than .20 were entered into a multivariable model (Table 2). In the multivariable model, older than age 60 years (odds ratio [OR] = 2.93, 95% CI = 1.46 to 5.88), weight loss greater than 5% during treatment (OR = 2.40, 95% CI = 1.12 to 5.14), and sarcopenia at baseline (OR = 17.21, 95% CI = 8.48 to 34.94) were statistically significantly associated with sarcopenia at 24 months after treatment completion, compared with age 60 years or younger, weight loss of 5% or less or weight gain during treatment, and no sarcopenia at baseline, respectively. Black race (OR = 0.29, 95% CI = 0.09 to 0.94) was protective compared with white race.
Table 2.
Univariate and multivariable logistic regression analysis of factors associated with long-term sarcopenia
| Characteristic | Univariate regression analysis | Multivariable regression analysis | ||
|---|---|---|---|---|
| OR (95% CI) | P* | OR (95% CI) | P* | |
| Age > 60 y | 3.09 (1.86 to 5.12) | <.001 | 2.93 (1.46 to 5.88) | .002 |
| Race | ||||
| White | Referent | – | Referent | – |
| Black | 0.37 (0.16 to 0.84) | .02 | 0.29 (0.09 to 0.94) | .04 |
| CHF, yes | 1.07 (0.61 to 1.87) | .82 | - | |
| COPD, yes | 1.78 (1.07 to 2.95) | .03 | 0.94 (0.46 to 1.89) | .85 |
| Diabetes, yes | 0.58 (0.35 to 0.97) | .04 | 0.76 (0.37 to 1.61) | .48 |
| No. of comorbidities | 1.09 (0.92 to 1.29) | .32 | - | |
| Stage III/IV | 1.72 (1.06 to 2.79) | .03 | 1.27 (0.65 to 2.47) | .48 |
| B-symptoms, yes | 0.95 (0.59 to 1.52) | .83 | - | |
| LDH, elevated | 1.13 (0.69 to 1.85) | .62 | - | |
| Weight change during treatment | ||||
| Loss ≤ 5% or gain | Referent | – | Referent | – |
| Loss > 5% | 1.47 (0.88 to 2.46) | .15 | 2.40 (1.12 to 5.14) | .02 |
| Baseline sarcopenia | 19.48 (10.65 to 35.60) | <.001 | 17.21 (8.48 to 34.94) | <.001 |
| Baseline BMI category | ||||
| <25 | Referent | – | Referent | – |
| 25 to < 30 | 0.31 (0.17 to 0.56) | <.001 | 0.52 (0.24 to 1.16) | .11 |
| ≥30 | 0.18 (0.09 to 0.35) | <.001 | 0.46 (0.18 to 1.18) | .11 |
Variables Associated With Long-Term Visceral Fat Gain
At 24 months, 25% of patients gained more than 75 cm in visceral fat area from baseline, corresponding to a 3.2 kg increase in visceral fat mass. To identify risk factors for visceral fat area increase of more than 75 cm, patients with both baseline and 24 month post-treatment scans were analyzed (n = 293). An exploratory univariate logistic regression analysis was initially performed, and variables with P values of less than .20 were entered into a multivariable model (Table 3). In the multivariable model, only weight gain greater than 5% during chemotherapy was statistically significantly associated with more than 75 cm visceral fat area gain from baseline (OR = 4.60, 95% CI = 2.42 to 8.74), compared with weight gain of 5% or less or weight loss.
Table 3.
Univariate and multivariable logistic regression analysis of factors associated with long-term visceral fat area gain > 75 cm2
| Characteristic | Univariate regression analysis | Multivariable regression analysis | ||
|---|---|---|---|---|
| OR (95% CI) | P* | OR (95% CI) | P* | |
| Age > 60 y | 0.95 (0.57 to 1.61) | .86 | - | |
| Race | ||||
| White | Referent | – | - | |
| Black | 0.96 (0.44 to 2.07) | .91 | - | |
| CHF, yes | 1.04 (0.56 to 1.93) | .89 | - | |
| COPD, yes | 1.38 (0.80 to 2.39) | .25 | - | |
| Diabetes, yes | 1.03 (0.60 to 1.78) | .92 | - | |
| No. of comorbidities | 0.94 (0.78 to 1.14) | .94 | - | |
| Stage III/IV | 2.56 (1.45 to 4.46) | .001 | 1.86 (0.98 to 3.52) | .06 |
| B-symptoms, yes | 2.26 (1.31 to 3.91) | .004 | 1.57 (0.85 to 2.90) | .15 |
| LDH, elevated | 1.72 (0.99 to 2.98) | .02 | 1.10 (0.60 to 2.04) | .76 |
| Weight change during treatment | ||||
| Gain ≤ 5% or loss | Referent | – | - | |
| Gain > 5% | 5.13 (2.82 to 9.34) | <.001 | 4.60 (2.42 to 8.74) | <.001 |
| Baseline sarcopenia | 1.27 (0.75 to 2.14) | .37 | - | |
| Baseline BMI category | ||||
| <25 | Referent | – | - | |
| 25 to < 30 | 0.71 (0.38 to 1.32) | .28 | - | |
| ≥30 | 0.66 (0.34 to 1.28) | .22 | - | |
Discussion
This is the first large study to examine short- and long-term body composition changes in DLBCL survivors following CHOP-based chemotherapy. In this cohort of largely white male veterans, weight loss during chemotherapy was driven primarily by the loss of muscle, with an average muscle area decrease of 3% from baseline over the course of treatment. Weight gain after chemotherapy was driven primarily by increases in body fat, with a 21% increase in both subcutaneous and visceral adipose tissue area from baseline by 24 months after treatment completion.
Total adipose tissue area increased by an average of 80 cm from baseline over the two years after treatment completion, corresponding to a 3.4 kg increase in total body fat mass. The magnitude of long-term fat gain observed in this study is much higher than that observed in a similarly aged population of healthy men (0.74 kg over two years) (23). This could be because of several reasons. First, most DLBCL patients lose weight in the year prior to diagnosis, with an average weight loss of 6% of their one year prediagnosis weight (4). Patients may therefore purposefully gain weight after treatment until they return to their perceived normal pretreatment weight. There is also evidence that cancer survivors have decreased physical activity levels after their diagnosis of cancer (24,25). The combination of increased caloric intake and decreased physical activity may contribute to the observed disproportionate gain of fat over muscle mass.
Our observation of short-term fat gain and muscle loss during treatment is consistent with a previously published study of thirty Serbian NHL patients (26). Using bioelectric impedance analysis to measure body composition, the authors found that patients gained a mean of 2.5 kg in body fat and lost a mean of 0.57 kg in lean body mass between the first and sixth cycles of chemotherapy. To our knowledge, there are no studies examining long-term body composition changes in the years following treatment completion in the NHL patient population. However, longitudinal studies in breast (27,28) and prostate cancer (29–32) have consistently reported unfavorable long-term body composition changes, with a disproportionate increase in fat mass over muscle mass.
Multiple threshold values used for defining sarcopenia exist in the literature. We chose to use the sex-specific threshold values reported by Martin et al. for overweight patients (19) because it is the largest study of sarcopenia in cancer patients to date and because 70% of our patients have a BMI of 25 or higher. We found that at treatment initiation 41% of DLBCL patients were sarcopenic. Our reported sarcopenia prevalence is roughly comparable with Martin et al., which found that 31% of male cancer patients were sarcopenic (19). However, it is lower than previous studies of the DLBCL population, which report a sarcopenia prevalence ranging from 60% to 66% in male patients (33,34). Unlike our study, these studies used different threshold values to define sarcopenia. They also studied different DLCBL patient populations, including the elderly and Japanese, who may have a lower mean muscle area than our cohort of United States veterans.
We can identify patients at highest risk for the long-term development of either visceral adiposity or sarcopenia based on clinical observations available by the end of treatment. This finding has important implications for survivorship care planning. Patients at highest risk for the long-term development of sarcopenia have sarcopenia at baseline and have lost greater than 5% weight during chemotherapy. These patients could be targeted for resistance training following treatment completion in order to increase their muscle mass (35). Patients at highest risk for long-term visceral fat gain are those who have gained more than 5% of weight during chemotherapy. This subset of patients could be targeted for weight loss and exercise interventions following treatment completion in order to prevent visceral fat gain (36,37). Studies have shown that cancer patients are uniquely motivated to implement lifestyle changes (38), making the immediate post-treatment period an ideal time for health behavior counseling.
While weight change during treatment is easily measured in the clinic setting, the measurement of baseline sarcopenia may be more difficult to implement. CT and PET/CT scans are routinely performed for staging purposes, and both commercial and free software for body composition analysis are readily available. However, CT image analysis involves several steps, including the manual selection of muscle area and is currently used mainly in the research setting. In order to facilitate use in clinical practice, future implementation efforts should focus on automating skeletal muscle measurement and providing this information to clinicians as part of a clinical decision support system.
There are multiple strengths to this study. First, the Veterans Health Administration provided a large study cohort with comprehensive clinical data drawn from patients diagnosed and treated throughout the United States. Second, patients had an equal opportunity for inclusion regardless of age or comorbidities that may introduce selection bias observed in studies of patients enrolled in clinical trials.
Limitations to this study should be noted. First, 40% of the original cohort of patients did not have CT scans available for analysis. To address concerns of selection bias, comparisons were made between patients with and without CT scans, which showed that baseline characteristics were similar between the two groups. Second, the European Working Group on Sarcopenia in Older People released a consensus definition on sarcopenia, which recommends using the presence of both low muscle mass and low muscle function to diagnose sarcopenia (39). Because of the retrospective nature of our study, we were unable to measure muscle function. Third, the study cohort was comprised almost entirely of Caucasian men, which may limit our ability to extrapolate these findings to women and other races. Fourth, the VHA patient population has a lower median income than that of the general population, which could result in more extreme average fat gain after treatment completion given the inverse relationship between income and obesity previously observed in the United States (40). Finally, there were no healthy, age-matched controls, limiting our ability to compare body composition changes of DLBCL survivors with that of the general population.
In conclusion, this is the first study to describe body composition changes during and after chemotherapy treatment in the DLBCL population. We demonstrate that DLBCL survivors undergo unfavorable long-term body composition changes, with a disproportionate gain of fat mass over muscle mass at a rate much higher than that observed in a healthy aging population. Patients at highest risk for the long-term development of either sarcopenia or visceral adiposity can be identified by clinical characteristics available at the end of treatment. These high-risk patients could be targeted for individualized lifestyle interventions designed to improve their body composition parameters.
Supplementary Material
Abstract
Background: Body composition parameters are associated with long-term health outcomes. We assessed longitudinal body composition changes in diffuse large B-cell lymphoma (DLBCL) survivors and identified clinical variables associated with the long-term development of sarcopenia and visceral obesity.
Methods: A retrospective cohort of United States veterans with DLBCL treated with cyclophosphamide, doxorubicin, vincristine, and prednisone, with or without rituximab, was assembled. Muscle, subcutaneous fat, and visceral fat areas were measured with computed tomography analysis. Data were analyzed with repeated-measures analysis of variance and logistic regression. All statistical tests were two-sided.
Results: Three hundred forty-two patients were included. Muscle area initially decreased during treatment, then returned to baseline by 24 months after treatment. Subcutaneous fat area increased from baseline by 6.5% (95% confidence interval [CI] = 2.6% to 10.5%) during treatment and by 21.4% (95% CI = 15.7% to 27.2%) by 24 months after treatment. Visceral fat area increased from baseline by 4.5% (95% CI = -0.9% to 9.9%) during treatment and by 21.6% (95% CI = 14.8% to 28.4%) by 24 months after treatment. Variables associated with long-term development of sarcopenia included: baseline sarcopenia (adjusted odds ratio [aOR] = 17.21, 95% CI = 8.48 to 34.94), older than age 60 years (aOR = 2.93, 95% CI = 1.46 to 5.88), and weight loss greater than 5% during treatment (aOR = 2.40, 95% CI = 1.12 to 5.14). Variables associated with long-term visceral fat gain included: weight gain greater than 5% during treatment (aOR = 4.60, 95% CI = 2.42 to 8.74).
Conclusions: DLBCL survivors undergo unfavorable long-term body composition changes. Patients at risk for the long-term development of sarcopenia or visceral obesity can be identified based on clinical risk factors and targeted for lifestyle interventions.
Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin’s lymphoma (NHL), accounting for 30% of all NHL cases (1,2). With many DLBCL patients surviving years after treatment (3), it is important to understand the long-term effects of chemotherapy, including its effects on weight and body composition. We recently described treatment-related weight changes in DLBCL survivors, demonstrating that the average patient initially experiences weight loss during treatment. This is followed by substantial weight gain in the 24 months after treatment, with a mean increase of 2.9 kg above pretreatment weight (4). However, the body composition changes associated with these observed weight changes, and the extent to which weight loss during treatment is the result of muscle loss and weight gain after treatment is because of fat deposition, are not well understood.
Body composition parameters are associated with long-term health outcomes, making treatment-related body composition changes an important issue for DLBCL survivorship. For example, visceral adiposity is a key component of the metabolic syndrome and has been associated with impaired glucose and lipid metabolism (5–8), hypertension (7,9), and increased risk of coronary artery disease and type 2 diabetes (10–13). Sarcopenia, or low muscle mass, has been associated with poorer functional outcomes and increased physical disability in older adults (14–16). As a result, knowledge of body composition changes occurring during the course of chemotherapy could play an integral role in guiding survivorship care planning and subsequent lifestyle interventions. This is particularly true in DLBCL patients who achieve complete remission as many of these patients will be cured of their disease.
In this study, we evaluate a cohort of US veterans with DLBCL diagnosed and treated within the United States Veterans Health Administration (VHA) system. The purpose of this study is to describe longitudinal body composition changes in DLBCL patients undergoing cyclophosphamide, doxorubicin, vincristine, and prednisone, with or without rituximab (CHOP +/− R) chemotherapy, and to identify factors associated with the long-term development of either sarcopenia or visceral adiposity.
Click here to view.References
- 1. Morton LM, Wang SS, Devesa SS, Hartge P, Weisenburger DD, Linet MS. Lymphoma incidence patterns by WHO subtype in the United States, 1992-2001. Blood. 2006;107(1):265–276.
- 2. Swerdlow S, Campo E, Harris N, et al. World Health Organization classification of tumours of haematopoietic and lymphoid tissues. 4th ed. Lyon: IARC Press; 2008. [PubMed]
- 3. Coiffier B, Thieblemont C, Van Den Neste E, et al. Long-term outcome of patients in the LNH-98.5 trial, the first randomized study comparing rituximab-CHOP to standard CHOP chemotherapy in DLBCL patients: a study by the Groupe d’Etudes des Lymphomes de l’Adulte. Blood. 2010;116(12):2040–2045.
- 4. O’Brian K, Luo S, Ganti A, et al. Short- and Long-term weight changes among United States veterans with diffuse large B-cell lymphoma treated with CHOP chemotherapy. Leuk Lymphoma. 2015;Jul 25:1–7.
- 5. Carr DB, Utzschneider KM, Hull RL, et al. Intra-abdominal fat is a major determinant of the National Cholesterol Education Program Adult Treatment Panel III Criteria for the metabolic syndrome. Diabetes. 2004;53(8):2087–2094. [[PubMed]
- 6. Despres J-P. Health consequences of visceral obesity. Ann Med. 2001;33(8):534–541. [[PubMed]
- 7. Fox CS, Massaro JM, Hoffmann U, et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116(1):39–48. [[PubMed]
- 8. Fujioka S, Matsuzawa Y, Tokunaga K, Tarui S. Contribution of intra-abdominal fat accumulation to the impairment of glucose and lipid metabolism in human obesity. Metabolism. 1987;36(1):54–59. [[PubMed]
- 9. Hayashi T, Boyko EJ, Leonetti DL, et al. Visceral adiposity is an independent predictor of incident hypertension in Japanese Americans. Ann Intern Med. 2004;140(12):992–1000. [[PubMed]
- 10. Fujimoto W, Bergstrom R, Boyko E, et al. Visceral adiposity and incident coronary heart disease in Japanese-American men. Diabetes Care. 1999;22(11):1808–1812. [[PubMed]
- 11. Nakamura T, Tokunaga K, Shimomura I, et al. Contribution of visceral fat accumulation to the development of coronary artery disease in non-obese men. Atherosclerosis. 1994;107(2):239–246. [[PubMed]
- 12. Nicklas BJ, Penninx BWJH, Cesari M, et al. Association of visceral adipose tissue with incident myocardial infarction in older men and women: The Health, Aging and Body Composition Study. Am J Epidemiol. 2004;160(8):741–749. [[PubMed]
- 13. Boyko EJ, Fujimoto WY, Leonetti DL, Newell-Morris L. Visceral adiposity and risk of type 2 diabetes. Diabetes Care. 2000;23(4):465–471. [[PubMed]
- 14. Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc. 2002;50(5):889–896. [[PubMed]
- 15. Janssen I. Influence of sarcopenia on the development of physical disability: The cardiovascular health study. J Am Geriatr Soc. 2006;54(1):56–62. [[PubMed]
- 16. Baumgartner RN, Koehler KM, Gallagher D, et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol. 1998;147(8):755–763. [[PubMed]
- 17. Morton LM, Turner JJ, Cerhan JR, et al. Proposed classification of lymphoid neoplasms for epidemiologic research from the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph). Blood. 2007;110(2):695–708.
- 18. Mourtzakis M, Prado CMM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab. 2008;33(5):997–1006. [[PubMed]
- 19. Martin L, Birdsell L, MacDonald N, et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol. 2013;31(12):1539–1547. [[PubMed]
- 20. Shen W, Punyanitya M, Wang Z, et al. Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol. 2004;97(6):2333–2338. [[PubMed]
- 21. World Health OrganizationGlobal database on body mass index. . Accessed January 9, 2015.[PubMed]
- 22. The International non-Hodgkin’s Lymphoma Prognostic Factors Project. A predictive model for aggressive non-Hodgkin’s lymphoma. N Engl J Med. 1993;329(14):987–994. [[PubMed]
- 23. Guo SS, Zeller C, Chumlea WC, Siervogel RM. Aging, body composition, and lifestyle: The Fels Longitudinal Study. Am J Clin Nutr. 1999;70(3):405–411. [[PubMed]
- 24. Irwin ML, Crumley D, McTiernan A, et al. Physical activity levels before and after a diagnosis of breast cancer: The Health, Eating, Activity, and Lifestyle (HEAL) study. Cancer. 2003;97(7):1746–1757.
- 25. Blanchard CM, Denniston MM, Baker F, et al. Do adults change their lifestyle behaviors after a cancer diagnosis?Am J Health Behav. 2003;27(3):246–256. [[PubMed]
- 26. Stanisavljevic NS, Marisavljevic DZ. Weight and body composition changes during R-CHOP chemotherapy in patients with non-Hodgkin’s lymphoma and their impact on dose intensity and toxicity. J BUON. 2010;15(2):290–296. [[PubMed]
- 27. Demark-wahnefried W, Peterson BL, Winer EP, et al. Changes in weight, body composition, and factors influencing energy balance among premenopausal breast cancer patients receiving adjuvant chemotherapy. J Clin Oncol. 2001;19(9):2381–2389. [[PubMed]
- 28. Irwin ML, McTiernan A, Baumgartner RN, et al. Changes in body fat and weight after a breast cancer diagnosis: influence of demographic, prognostic, and lifestyle factors. J Clin Oncol. 2005;23(4):774–782.
- 29. Berruti A, Dogliotti L, Terrone C, et al. Changes in bone mineral density, lean body mss and fat content as measured by dual energy x-ray absorptiometry in patients with prostate cancer without apparent bone metastases given androgen deprivation therapy. J Urol. 2002;167(6):2361–2367. [[PubMed]
- 30. Smith MR, Finkelstein JS, McGovern FJ, et al. Changes in body composition during androgen deprivation therapy for prostate cancer. J Clin Endocrinol Metab. 2002;87(2):599–603. [[PubMed]
- 31. Lee H, McGovern K, Smith MR. Changes in bone mineral density and body composition during initial and long-term gonadotropin-releasing hormone agonist treatment for prostate carcinoma. Cancer. 2005;104(8):1633–1637. [[PubMed]
- 32. Smith MR. Changes in fat and lean body mass during androgen-deprivation therapy for prostate cancer. Urology. 2004;63(4):742–745. [[PubMed]
- 33. Lanic H, Kraut-Tauzia J, Modzelewski R, et al. Sarcopenia is an independent prognostic factor in elderly patients with diffuse large B-cell lymphoma treated with immunochemotherapy. Leuk Lymphoma. 2014;55(4):817–823. [[PubMed]
- 34. Nakamura N, Hara T, Shibata Y, et al. Sarcopenia is an independent prognostic factor in male patients with diffuse large B-cell lymphoma. Ann Hematol. 2015;94(12):2043–2053. [[PubMed]
- 35. Burton LA, Sumukadas D. Optimal management of sarcopenia. Clin Interv Aging. 2010;5:217–228.
- 36. Kay S, Fiatarone Singh M. The influence of physical activity on abdominal fat: a systematic review of the literature. Obes Rev. 2006;7(2):183–200. [[PubMed]
- 37. Chaston TB, Dixon JB. Factors associated with percent change in visceral versus subcutaneous abdominal fat during weight loss: findings from a systematic review. Int J Obes (Lond). 2008;32(4):619–628. [[PubMed]
- 38. Satia JA, Campbell MK, Galanko JA, James A, Carr C, Sandler RS. Longitudinal changes in lifestyle behaviors and health status in colon cancer survivors. Cancer Epidemiol Biomarkers Prev. 2004;13(6):1022–1031. [[PubMed]
- 39. Cruz-Jentoft A, Baeyens J, Bauer J, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39(4):412–423.
- 40. Wang Y, Beydoun MA. The obesity epidemic in the United States - gender, age, socioeconomic, racial/ethnic, and geographic characteristics: A systematic review and meta-regression analysis. Epidemiol Rev. 2007;29(1):6–28. [[PubMed]