1. Introduction
Obesity and being overweight (BMI ≥ 25 kg/m2) are common risk factors for most cancers. According to the National Cancer Institute (NCI) and the International Agency for Research on Cancer (IARC), obesity and being overweight increase the risk of at least 13 cancers, including breast, colorectal, pancreatic, stomach, gallbladder, liver, thyroid, ovarian, endometrial, kidney, multiple myeloma, esophageal, and meningeal cancers [1,2]. However, patients with a higher body mass index (BMI) among the overweight to moderate obesity range (>25 kg/m2–<35 kg/m2) paradoxically have a lower overall mortality rate after a cancer diagnosis, a phenomenon known as the “obesity paradox” [3]. Most epidemiological studies use BMI to measure overweight and obesity. However, BMI does not distinguish between fat mass and lean mass [4]. In addition, a low BMI may mask excess fat, and a high BMI may mask high lean mass [4]. To overcome these limitations, recent studies recommend using imaging techniques, such as dual-energy X-ray absorptiometry (DXA) and computed tomography (CT), to measure body composition [3,5].
DXA takes advantage of the difference in the X-ray attenuation of tissue and bone at different X-ray energies in order to measure lean body mass (LBM), fat mass (FM), and bone mineral mass (BMM), which can be extrapolated to the entire body. DXA has been used in many epidemiological studies for body composition measurements since it produces less radiation exposure and is less expensive than other methods, such as CT and magnetic resonance imaging (MRI) [5,6,7]. Body composition is an important measure after a cancer diagnosis. It measures components such as fat mass and skeletal muscle mass index. Skeletal muscle index is associated with the poor prognosis of various cancers and chemotherapy-related toxicities [8,9,10,11]. Both fat mass and skeletal muscle index have been observed to be associated with survival in cancer patients. Although most of the recent literature examining the association between body composition, all-cause mortality (death due to any cause) and cancer-specific mortality (deaths with cancer as the underlying cause), and using imaging techniques, has found conflicting results, most studies observed that high skeletal muscle mass and low adiposity levels were associated with increased survival in cancer patients [4,12,13,14,15]. Potential explanations for the variation are that most studies in this area had small sample sizes, short follow-ups, and a select population.
One critical but underexplored area of obesity research is on the impact of race on the association between body composition and mortality. Previous studies have observed racial differences in fat mass and skeletal muscle mass [16,17]. There are very few studies evaluating the association between body composition and mortality by race, and the majority of those studies have used BMI to assess body composition [18,19]. These studies observed no significant difference in the association between body composition and mortality among Black people and White people [18,19]. In our study, we aim to examine the associations between DXA-derived total fat mass and appendicular skeletal muscle mass index, and overall and cancer-specific mortality in cancer survivors. We will also investigate whether the association between these measures of body composition and mortality differ by race.
2. Materials and Methods
The National Health and Nutrition Examination Survey (NHANES) is a multistage, cross-sectional survey conducted in the United States (U.S.); NHANES is designed to monitor the nutritional status and health of a civilian, non-institutionalized U.S. population. Participants from approximately 30 U.S. counties are selected every couple of years to complete a personal, structured interview at home, and undergo standardized laboratory examinations and laboratory tests in mobile health centers. NHANES questionnaires cover a multitude of topics, including demographic characteristics, general health, lifestyle, and disease history. In these secondary analyses, we used 1999–2006 and 2011–2018 NHANES data, which were linked to death certificate records from the National Death Index (NDI). Follow-up began at the time of the baseline interview and continued until death, last contact, or 31 December 2019. The eligibility criteria for this study included participants who (1) underwent DXA examination to measure body composition, (2) had cancer history, and (3) had survived for at least 1 year after cancer diagnosis, since the acute effects of cancer treatment substantially impact nutrition-related measures [20]. A total of 1682 cancer survivors were included in the present study. Additional details regarding participant selection are in the Figure 1.
2.1. Exposures of Interest
Whole-body dual-energy X-ray absorptiometry (DEXA) scans were used to measure total fat mass and appendicular skeletal mass, which consists of the upper and lower limbs’ muscle mass. Total fat mass was categorized into tertiles (low, medium, and high) with 7094.3–23,548.8 g, 23,548.9–32,209.9 g, and 322,010–99,662.0 g as cut-offs for low, moderate, and high fat mass, respectively. We assessed high vs. low total fat mass. A standard protocol [21] was used by trained and certified radiology technologists to perform the DEXA scans. The appendicular skeletal muscle mass was used to determine the skeletal muscle index (ASMI), calculated by skeletal muscle mass/height squared (ASM/height2) in kg/m2. Appendicular skeletal muscle mass was calculated using the sum of the lower and upper limbs [22]. A trained stadiometer measured the participants’ height without shoes. The European Working Group on Sarcopenia in Older People (EWGSOP) criteria (i.e., ASMI less than 5.45 kg/m2 for females and less than 7.26 kg/m2 for males) were used to define sarcopenia [20]. We also included other proxy measures for body composition, such as body mass index (BMI) and waist circumference (WC).
2.2. Outcomes
The outcomes of interest were (1) cancer all-cause mortality, (2) cancer-specific mortality, and (3) obesity-related cancer mortality. Obesity-related cancer mortality was defined as adjudicated deaths attributed to breast, colorectal, pancreatic, stomach, gallbladder, liver, thyroid, ovarian, endometrial, kidney, multiple myeloma, or esophageal cancer, according to the National Cancer Institute [2]. Death was identified by linkage to the NDI through 31 December 2019. The cause of death was ascertained by the International Classification of Diseases, Tenth Revision (ICD-10).
2.3. Other Covariates
Demographic characteristics, such as age (<50, 50–64, 65–74, or ≥75 years), race (non-Hispanic white people, non-Hispanic Black people, or others, including Asians, Mexican Americans and multiracial groups), sex (male or female), education (high school or less, attended college, or graduated from college), and marital status (not married or married) were obtained through self-report. Participants were categorized as never smokers, current smokers, or former smokers. Smoking has been seen to increase the risk of sarcopenia [5]. Daily energy intake was measured in 24-h food recall at the baseline interview and was categorized into approximate quartiles (<1360 kcal/day, 1361 to 1785 kcal/day, 1786 to 2348 kcal/day, >2349 kcal/day). In our study, we incorporated the histories of 4 self-reported diseases (heart attack, stroke, coronary heart disease, and congestive heart failure). The time that had elapsed since cancer diagnosis was categorized into (1–3, 4–6, ≥7) years. Study variables were selected based on prior knowledge of the relationship between the exposures and outcomes of interest [6,7,8].
2.4. Statistical Analysis
We summarized the distribution of the cancer survivors’ demographic characteristics by tertiles of total fat mass. We then elucidated the number of deaths, person-year during follow-up, and mortality rate among the cancer survivors. Age and multivariable-adjusted hazard ratios (aHRs), as well as the corresponding 95% confidence intervals (CIs) for the risk of all-cause and cancer-related mortality associated with tertiles of fat mass and presence of sarcopenia, were computed using Cox proportional hazard models. When the model’s outcome was all-cause mortality, participants who were classified as either alive or deceased from non-cancer-related causes were censored. Multiple covariates were adjusted for in the proportional hazard models, such as age, sex, education, marital status, race, smoking, energy intake, and comorbidity burden. In addition, we further corrected for NHANES sampling weight. There was no violation of the proportional hazards assumption when assessed based on scaled Schoenfield residuals.
Subgroup analyses were based on age (<50 vs. ≥50 years), sex (female vs. male), energy intake (<1785 vs. ≥1785 kcal/day), and comorbidities (0 vs. ≥1 comorbidity). A Wald test was used to identify whether the interaction between these factors and the outcomes of interest were significant. Kaplan–Meier curves were used to visualize risk for all-cause and cancer-specific mortality by total fat mass and sarcopenia in cancer survivors. In addition, we stratified by (i) cancer types according to whether or not they were related to obesity and (ii) race (Non-Hispanic White people and Non-Hispanic Black people) and examined the associations of total fat mass and sarcopenia with all-cause and cancer-specific mortality. To assess the potential nonlinear associations of total fat mass and sarcopenia with all-cause mortality, and to capture the variation in risk across the entire continuum of the relations, we used restricted cubic splines with 4 knots. Two-sided p-values of less than 0.05 determined the statistical significance. These statistical analyses were performed using SAS v9.4 (SAS Institute Inc., Cary, NC, USA) and R statistical software (version 4.0.2).
3. Results
In our study, the median follow-up time was 9.7 years. Participants included 1682 adult cancer survivors (Figure 1). A total of 668 (39.7%) of the cancer survivors died during follow-up. Cancer-specific deaths were observed in the 213 (15.6%) of the study participants. The mean age of the study participants was 61.9 years. In our study sample, the prevalence of sarcopenia was 25.0% (N = 304) (Table 1). The mean total fat mass was 29,400 (range = 7094–99,662) and mean ASMI was 7.28 (range = 3.74–13.91 kg/m2). Most of the participants were males, 56.2% (N = 945), and White 72.1% (N = 1213). Participants with high school or less education had a higher prevalence of total fat mass compared to participants who had graduated from college (48.8% vs. 25.0%). Participants who were current smokers had a higher total fat mass compared to former smokers (35.2% vs. 4.1%).
In the age-adjusted Cox proportional hazards regression models (Table 2), high total fat mass (Adjusted Hazard Ratio; aHR = 1.76, [95% CI = 1.45–2.13], model 1) and sarcopenia (aHR = 1.58, [95% CI = 1.28–1.94], model 1) were associated with a higher risk of all-cause mortality. In the multivariable Cox models (Table 2), a higher total fat mass (aHR = 1.30, [95% CI = 1.06–1.61], model 2) and sarcopenia (aHR = 1.51, [95% CI = 1.22–1.88], model 2) were associated with a higher risk of all-cause mortality in cancer survivors. Correction for NHANES sampling weight did not substantially alter the results. In addition, in the age-adjusted Cox proportional hazards regression model, a high total fat mass (aHR = 1.42, [95% CI = 1.01–2.00], model 1) and sarcopenia (aHR = 1.88, [95% CI = 1.33–2.65], model 1) were associated with a higher risk of cancer-specific mortality. In the multivariable model Cox models, sarcopenia (aHR = 1.74, [95% CI = 1.23–2.29], model 2) was associated with a higher risk of cancer-specific mortality in cancer survivors; however, total fat mass was not associated with cancer-specific mortality.
For Table 3, participants were classified according to whether or not they had obesity-related cancers. In the age-adjusted Cox proportional hazard models, total fat mass (aHR = 1.81, [95% CI = 1.34–2.43], model 1) and sarcopenia (aHR = 1.49, [95% CI = 1.09–2.03], model 1) were associated with a higher risk of all-cause mortality in obesity-related cancer survivors; however, total fat mass was not associated with all-cause mortality in non-obesity-related cancer survivors. The results did not change significantly after adjusting for the covariates. There was no association between total fat mass and the risk of mortality in obesity-related and non-obesity-related cancer patients. In the multivariable Cox models, sarcopenia (aHR = 1.76, [95% CI = 1.03–3.02], model 2) was associated with a higher risk of all-cause mortality in obesity-related cancer survivors. When participants were stratified according to race, high total fat mass was associated with an increased risk of mortality after adjusting for covariates (aHR = 1.37, [95% CI = 1.07–1.75], model 2, Table 4); however, total fat was not associated with all-cause mortality in Black people. The association between sarcopenia and all-cause mortality was twice as strong in Black people (aHR = 2.99, [95% CI = 1.39–6.06], model 2) compared to Whites (aHR = 1.53, [95% CI = 1.19–1.95], model 2).
Kaplan–Meier curves suggested that participants with a high total fat mass (Figure 2A) and those with sarcopenia (Figure 2B) had a higher risk for all-cause mortality. In subgroup analyses for cancer survivors (Supplemental Table S1), we found that total fat mass significantly interacts with sex and age in relation to all-cause mortality. Specifically, the effect size of total fat mass was significantly larger in males (female: aHR = 1.53, 95% CI = 0.92–2.55; male: aHR = 2.16, 95% CI = 1.15–4.06; p-interaction < 0.001). For supplemental Table S2, a higher total fat mass (aHR = 1.33, [95% CI = 1.03–1.72], model 2) and sarcopenia (aHR = 1.46, [95% CI = 1.11–1.91], model 2) were associated with a higher risk of non-cancer mortality after adjusting for covariates. Also, we observed a J-shaped positive association between total fat mass and all-cause mortality (Figure 3).
4. Discussion
In this study, we used a nationally representative sample of 1682 cancer survivors with a mean age of 61.9 years, who were followed up for an average period of 9.67 years. We identified positive associations between both total fat mass and sarcopenia, and all-cause and cancer-specific mortality. Participants with a higher fat mass and sarcopenia had a 30% and 51% higher risk of all-cause mortality risk, respectively. Sarcopenia was also associated with a higher risk of cancer-specific mortality; however, total fat mass was not associated with cancer-specific mortality. In addition, we found that a high total fat mass was associated with an increased risk of mortality in White people, but not in Black people. The association between sarcopenia and all-cause mortality was about twice as strong in Black people compared to White people. Among obesity-related cancer survivors, total fat mass was associated with a higher risk of all-cause but not cancer-specific mortality; however, among non-obesity cancer survivors, total fat mass was not associated with either all-cause or cancer-specific mortality. The outcomes of the subgroup analyses indicate that the magnitude of the association between total fat mass and risk of all-cause death was stronger in male than in female cancer survivors.
While previous studies examining the association between fat mass and all-cause mortality have found inconsistent results, most have observed a J- or U-shaped association between fat mass and mortality [23,24,25,26,27]. Other studies have identified an inverse relationship between fat mass and all-cause mortality [28,29], while some studies found no association [30,31,32]. In this study, we observed a J-shaped positive association between total fat mass and all-cause mortality. Although studies examining the association between total fat mass and mortality among cancer survivors are limited, several studies have observed direct positive associations between subcutaneous, visceral adipose tissues (VAT) [33,34] and total adipose tissue [4], and all-cause mortality, which is consistent with our findings. Nevertheless, meta-analysis of six studies examining the association between VAT and overall survival among prostate cancer patients found no association [35]. In addition, in a recently published meta-analysis of 128 studies, En Cheng and colleagues found that images measuring visceral, subcutaneous, and total adiposity were not associated with an increased risk of mortality among cancer survivors; however, high heterogeneity was observed among cancer types [36]. Reasons for the discrepancies in the fat mass-mortality association in the literature include the restriction to an elderly population [28,29,30], short follow-up periods [14,15,23,37], failure to adjust for important confounders [15,32,38], failure to assess the shape of the association using spline or other techniques [29,32,39], lack of mutual adjustment for fat mass [15,40], use of different techniques to assess fat mass, and measurement errors.
An important aspect of the fat mass-mortality association is whether the relationship differs by race. Previous studies assessing racial differences in the fat mass-mortality relationship have used BMI as a measure of body composition, despite its limitations. In this study, we found that high total fat mass was associated with an increased risk of mortality in White people; however, total fat mass was not associated with all-cause mortality in Black people. Numerous studies have assessed the association between BMI and mortality in White people [41,42,43,44,45,46,47,48]. In contrast, few studies have evaluated the BMI–mortality association in Black people [49,50,51]. Of those which have, most studies have observed a stronger BMI–mortality association in White people compared to Black people [41,51]. A few studies compared the BMI–mortality association in both Black people and White people. One of those studies was a large prospective study by Calle and colleagues; in this study, the association between BMI and mortality appeared to be weaker in Black people compared to White people after a 14-year follow-up [52]. Another large prospective study observed that the magnitude of the BMI–mortality association was similar in White people and Black people. In other studies, although Black people have been known to have a lower prevalence of sarcopenia than White people, Black people have a higher mortality rate [53]; other studies have shown that socioeconomic factors (such as income and educational status) may play a significant role in this observation [54,55]. Several physiological mechanisms underlying the association of fat mass with tumor progression and mortality have been proposed. High fat mass may lead to high circulating insulin and/or insulin-like growth factor 1, altered levels of leptin, and tissue-level inflammation. These factors downgrade anti-tumor immunity and increase the risk of tumor angiogenesis, growth, and metastasis. Metastasis is known to be responsible for 90% of cancer deaths [56].
Skeletal muscle mass is an important prognostic factor among cancer patients as it is associated with the quality of muscles, activity levels, and treatment-related toxicities. In this study, sarcopenia was measured by the skeletal muscle mass index and was found to be associated with all-cause mortality and cancer-related mortality in cancer survivors. In line with this finding, a higher skeletal muscle mass has been observed to increase the risk of mortality among prostate cancer [57], colorectal cancer [58,59,60], breast cancer [4,61], and advanced cancer [13] patients. In addition, skeletal muscles may be associated with insulin growth factor signaling, host immunity, systematic inflammation, and hormonal activity, which are related to poor cancer prognosis [62]. In addition, sarcopenia may indicate a progressive withdrawal of anabolism and increased catabolism [63], which are linked to functional decline, frailty, and a shorter life expectancy [64].
Our study has several strengths in design and analysis. First, it was conducted in a nationally representative sample. Second, total fat mass and sarcopenia were measured by DEXA. Third, vital status and cause of death were assessed by linkage to NDI and ICD codes, respectively, ensuring the validity of exposure and outcome of interest. Fourth, we also used other proxy measures of fat mass, such as BMI and waist circumference, to assess their associations with mortality. Finally, we assessed the shape of the association and dose–response relationship between the predictors and outcomes using restricted cubic splines in the Cox proportional hazards regression models. However, the results from this study should be interpreted in light of the following limitations. Most covariates, such as age, cancer types, smoking status, and comorbidity, were all self-reported, making them less valid compared to medical records due to their higher likelihood of recall bias. We also did not use a control for cancer treatment. Since cancer treatment can affect fat and skeletal muscle mass, as well as mortality, not controlling for this variable and other potential confounders could have led to residual confounding. In addition, the study has a relatively small sample and short follow up period of less than 10 years. In addition, since this is an observational study, we cannot infer causation.
5. Conclusions
In this nationally representative sample of US adults, we found opposing relations between fat mass, appendicular skeletal muscle mass and mortality. Moreover, we found that the body composition–mortality association differed by race. Fat and skeletal muscle mass may act differently on health outcomes, including mortality, which has long been used to explain the existence of the “obesity paradox”. Our results have significant clinical and public health implications, since fat and muscle mass are important prognostic factors in cancer patients. The findings also highlight the importance of healthy body composition, instead of focusing on weight alone. There is a need for more research into the effects of specific interventions, such as physical activities, a healthy diet, and supplemental nutrition aimed at reducing fat mass and preventing muscle loss.
L.A.-P. conceptualized and designed the study, analyzed and data, and drafted the manuscript. S.D.K. conceptualized and designed the study, edited the manuscript. M.W., D.Y. and C.W. assisted in study design and edited the manuscript. Y.-R.H., T.M.M., J.C.F. and T.-Y.D.C. conceptualized the study, provided feedback on statistical analyses, and critically reviewed the manuscript. D.B. conceptualized and designed the study, edited the manuscript, and provided funding. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The datasets generated and/or analyzed during the current study are publicly available from the Centers for Disease Control and Prevention (
We would like to acknowledge these federal agencies for data collection and management: the centers for Disease Control and Prevention and the National Center for Health Statistics, as well as the participants who contributed time and data to the National Health and Nutrition Examination Survey. We would also like to acknowledge Joel Divaker for his contribution to this article.
The authors declare that they have no competing interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 2. (A). Kaplan–Meier curve for total fat mass and all-cause mortality; (B). Kaplan–Meier curve for sarcopenia and all-cause mortality.
Enlarge this image.Figure 2. (A). Kaplan–Meier curve for total fat mass and all-cause mortality; (B). Kaplan–Meier curve for sarcopenia and all-cause mortality.
Enlarge this image.Figure 3. (A) Spline functions with corresponding 95% CIs from Cox proportional hazards regression for the relations of sarcopenia; (B) Spline functions with corresponding 95% CIs from Cox proportional hazards regression for the relations of total fat mass.
Enlarge this image.Figure 3. (A) Spline functions with corresponding 95% CIs from Cox proportional hazards regression for the relations of sarcopenia; (B) Spline functions with corresponding 95% CIs from Cox proportional hazards regression for the relations of total fat mass.
Summary of study characteristics, N = 1682.
| Cancer Survivors (N = 1682) |
||||
|---|---|---|---|---|
| Overall | Low Total Fat Mass | Moderate Total Fat Mass | High Total Fat Mass | |
| Participant Characteristics | 1682 (100) | 561 (33.3) | 560 (33.3) | 561 (33.3) |
| Age (year) | ||||
| <50 | 402 (23.9) | 161 (28.7) | 101 (18.0) | 140 (25.0) |
| 50–64 | 421 (25.0) | 169 (30.1) | 127 (22.7) | 125 (22.3) |
| 65–74 | 426 (25.3) | 150 (26.7) | 154 (27.5) | 122 (21.7) |
| ≥75 | 433 (25.7) | 81 (14.4) | 178 (31.8) | 174 (31.0) |
| Sex | ||||
| Female | 737 (43.8) | 164 (29.2) | 255 (45.5) | 318 (56.7) |
| Male | 945 (56.2) | 397 (70.8) | 305 (54.5) | 243 (43.3) |
| Race | ||||
| White | 1213 (72.1) | 380 (67.7) | 410 (73.2) | 423 (75.4) |
| Black | 216 (12.8) | 94 (16.8) | 58 (10.4) | 64 (11.4) |
| Other | 253 (15.0) | 87 (15.5) | 92 (16.4) | 74 (13.2) |
| Education | ||||
| High school or less | 810 (48.2) | 272 (48.5) | 265 (47.3) | 273 (48.8) |
| Attended college | 473 (28.2) | 179 (31.9) | 148 (26.4) | 146 (26.1) |
| Graduated from college | 397 (23.6) | 110 (1.6) | 147 (26.3) | 140 (25.0) |
| Marital status | ||||
| Not married | 672 (40.6) | 235 (42.4) | 215 (39.2) | 222 (40.1) |
| Married or living with partner | 983 (59.4) | 319 (57.6) | 333 (60.8) | 331 (59.9) |
| Smoking status | ||||
| Never | 664 (68.2) | 223 (70.3) | 229 (74.4) | 212 (60.7) |
| Current | 275 (28.2) | 82 (25.9) | 70 (22.7) | 123 (35.2) |
| Former | 35 (3.6) | 12 (3.8) | 9 (2.9) | 14 (4.0) |
| BMI | ||||
| Underweight | 28 (2.1) | 28 (6.2) | 0 (0.0) | 0 (0.0) |
| Normal | 411 (30.7) | 307 (67.9) | 103 (22.4) | 1 (0.2) |
| Overweight | 478 (35.8) | 116 (25.7) | 284 (61.7) | 78 (18.4) |
| Obese | 420 (31.4) | 1 (0.2) | 73 (15.9) | 346 (81.4) |
| Energy intake (kcal/day) | ||||
| <1359.0 | 345 (26.4) | 117 (28.0) | 114 (25.5) | 114 (25.9) |
| 1360.0–1784 | 323 (24.7) | 107 (25.6) | 112 (25.1) | 104 (23.6) |
| 1785–2348 | 354 (27.1) | 112 (26.8) | 120 (26.8) | 122 (27.7) |
| ≥2349.0 | 284 (21.7) | 82 (19.6) | 101 (22.6) | 101 (22.9) |
| ASMI | ||||
| Without sarcopenia | 913 (75.0) | 326 (88.8) | 328 (78.3) | 259 (60.1) |
| With sarcopenia | 304 (25.0) | 41 (11.2) | 91 (21.7) | 172 (39.9) |
| No. comorbidities | ||||
| 0 | 519 (30.9) | 135 (24.1) | 178 (31.8) | 206 (36.7) |
| 1 | 450 (26.8) | 164 (29.2) | 156 (27.9) | 130 (23.2) |
| ≥2 | 713 (42.4) | 262 (46.7) | 226 (40.4) | 225 (40.1) |
| Stroke | ||||
| No | 1566 (93.3) | 525 (93.6) | 522 (93.7) | 519 (92.5) |
| Yes | 113 (6.7) | 36 (6.4) | 35 (6.3) | 42 (7.5) |
| Coronary heart disease | ||||
| No | 1215 (89.8) | 395 (91.4) | 418 (89.7) | 402 (88.4) |
| Yes | 138 (10.2) | 37 (8.6) | 48 (10.3) | 53 (11.6) |
| Congestive heart failure | ||||
| No | 1252 (91.5) | 401 (90.7) | 431 (91.7) | 420 (91.9) |
| Yes | 117 (8.5) | 41 (9.3) | 39 (8.3) | 37 (8.1) |
| Heart attack | ||||
| No | 1521 (90.9) | 520 (93.4) | 499 (89.4) | 502 (89.8) |
| Yes | 135 (9.1) | 37 (6.6) | 59 (10.6) | 57 (10.2) |
| Continuous variables, mean (SD) | ||||
| Waist circumference | 99.4 (15.0) | 113 (12.9) | 98.4 (9.3) | 87.1 (9.4) |
| Height | 167 (9.8) | 167 (9.6) | 167 (10.2) | 167 (9.7) |
| Weight | 79.5 (19.2) | 97.0 (17.3) | 76.8 (12.0) | 64.8 (11.4) |
ASMI: Appendicular skeletal muscle mass index.
Association between DXA measures of body composition and risk of all-cause and cancer-specific mortality in cancer survivors.
| No. Death/Person-Years | Age-Adjusted HR (95% CI) | aHR (95% CI) | aHR (95% CI) | |
|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | ||
| All-cause mortality | ||||
| Total fat mass | ||||
| Low | 169/1602.3 | Ref | Ref | Ref |
| Medium | 237/2144.8 | 1.48 (1.21–1.80) | 1.09 (0.89–1.34) | 1.09 (0.89–1.34) |
| High | 262/2207.4 | 1.76 (1.45–2.13) | 1.30 (1.06–1.61) | 1.31 (1.06–1.61) |
| Appendicular skeletal muscle mass | ||||
| Without sarcopenia | 253/8624.7 | Ref | Ref | Ref |
| With sarcopenia | 155/2422.8 | 1.58 (1.28–1.94) | 1.51 (1.22–1.88) | 1.60 (1.28–1.99) |
| Waist circumference | ||||
| Normal | 211/6320.0 | Ref | Ref | Ref |
| High | 457/8801.2 | 0.85 (1.17–0.73) | 1.23 (0.98–1.54) | 1.01 (0.82–1.47) |
| Body Mass Index | ||||
| Normal | 219/4450.1 | Ref | Ref | Ref |
| obese | 171/5007.4 | 0.68 (0.58–0.80) | 0.71 (0.50–1.10) | 0.66 (0.41–1.14) |
| Cancer-specific mortality | ||||
| Total fat mass | ||||
| Low | 58/4768.8 | Ref | Ref | Ref |
| Medium | 72/4416.3 | 1.04 (0.73–1.47) | 1.01 (0.71–1.44) | 0.99 (0.70–1.42) |
| High | 83/4195.8 | 1.42 (1.01–2.00) | 1.24 (0.87–1.77) | 1.23 (0.86–1.75) |
| Appendicular skeletal muscle mass | ||||
| Without sarcopenia | 83/7840.7 | Ref | Ref | Ref |
| With sarcopenia | 56/1976.5 | 1.88 (1.33–2.65) | 1.74 (1.23–2.29) | 1.84 (1.28–2.65) |
| Waist circumference | ||||
| Normal | 66/6320.0 | Ref | Ref | Ref |
| High | 147/8801.2 | 0.72 (0.54–0.96) | 0.67 (0.50–0.90) | 0.62 (0.43–0.89) |
| Body Mass Index | ||||
| Normal | 82/4737.7 | Ref | Ref | Ref |
| Overweight or obese | 126/10,104.8 | 0.63 (0.48–0.83) | 0.58 (0.43–0.78) | 0.51 (0.35–0.73) |
Abbreviations: aHR: adjusted hazard ratio, CI: confidence interval. Mean fat mass; low = 18,571.7 g, moderate = 27,678.4 g, and high = 41,946.0 g. Model 1: The model adjusted for age. Model 2: The model adjusted for the following covariates: age, sex, race, marital status, education, energy intake, smoking status, comorbidity burden, and history of more than 1 cancer (for cancer survivors). Model 3: The model adjusted for the same covariates as Model 2 and additionally corrected for NHANES sampling weight.
Association between DXA measures of body composition and risk of all-cause and cancer-specific mortality in obesity and non-obesity-related cancer survivors.
| Measures of Body Composition | Obesity-Related Cancers (n = 699) | Non-Obesity-Related Cancers (n= 983) | ||
|---|---|---|---|---|
| Model 1 |
Model 2 |
Model 1 |
Model 2 |
|
| All-cause mortality | ||||
| Total fat mass | ||||
| Low | Ref | Ref | Ref | Ref |
| Medium | 1.14 (0.84–1.54) | 1.26 (0.93–1.71) | 1.02 (0.77–1.35) | 0.99 (0.74–1.32) |
| High | 1.81 (1.34–2.43) | 1.69 (1.23–2.32) | 1.27 (0.97–1.67) | 1.14 (0.86–1.52) |
| Appendicular skeletal muscle mass | ||||
| Without sarcopenia | Ref | Ref | Ref | Ref |
| With sarcopenia | 1.49 (1.09–2.03) | 1.53 (1.10–2.13) | 1.79 (1.35–2.36) | 1.61 (1.18–2.19) |
| Cancer-specific mortality | ||||
| Whole body fat mass | ||||
| Low | Ref | Ref | Ref | Ref |
| Medium | 1.09 (0.65–1.81) | 1.08 (0.65–1.81) | 1.00 (0.60–1.67) | 0.99 (0.59–1.67) |
| High | 1.51 (0.91–2.52) | 1.42 (0.84–2.41) | 1.54 (0.95–2.48) | 1.35 (0.81–2.23) |
| Appendicular skeletal muscle mass | ||||
| Without sarcopenia | ||||
| With sarcopenia | Ref | Ref | Ref | Ref |
| 1.65 (0.98–2.77) | 1.76 (1.03–3.02) | 2.23 (1.38–3.62) | 1.80 (1.04–3.12) | |
Abbreviations: aHR: adjusted hazard ratio, CI: confidence interval. Model 1: The model adjusted for age. Model 2: Age, sex, race, education, marital status, smoking status, energy intake, burden of comorbidities, history of more than 1 cancer (for cancer survivors) were adjusted for in this model. For cancer survivors, time elapsed since cancer diagnosis was stratified in this model.
Associations between DXA measures between total fat mass and sarcopenia with risk of all-cause mortality in cancer survivors by race.
| Measures of Body Composition | Whites | Blacks | ||
|---|---|---|---|---|
| Model 1 |
Model 2 |
Model 1 |
Model 2 |
|
| All-cause mortality | ||||
| Total fat mass | ||||
| Low | Ref | Ref | Ref | Ref |
| Medium | 1.27 (0.97–1.56) | 1.24 (0.97–1.57) | 1.02 (0.98–0.59) | 0.78 (0.44–1.38) |
| High | 1.50 (1.18–1.50) | 1.37 (1.07–1.75) | 1.80 (1.12–1.78) | 1.27 (0.73–2.22) |
| Appendicular skeletal muscle mass index | ||||
| Without sarcopenia | Ref | Ref | Ref | Ref |
| With sarcopenia | 1.57 (1.23–1.98) | 1.53 (1.19–1.95) | 2.76 (1.44–5.32) | 2.99 (1.39–6.06) |
Abbreviations: aHR: adjusted hazard ratio, CI: confidence interval. Mean total fat mass for Black people = 31,234.8 g and White people = 28,958.0. Model 1: The model adjusted for age. Model 2: In addition to age, the following covariates were adjusted for in this model: sex, race, education, marital status, smoking status, energy intake, burden of comorbidities, history of more than one cancer (for cancer survivors). This model was stratified by time elapsed since cancer diagnosis for cancer survivors.
Supplementary Materials
The following supporting information can be downloaded at:
References
1. Lauby-Secretan, B.; Scoccianti, C.; Loomis, D.; Grosse, Y.; Bianchini, F.; Straif, K. Body Fatness and Cancer—Viewpoint of the IARC Working Group. N. Engl. J. Med.; 2016; 375, pp. 794-798. [DOI: https://dx.doi.org/10.1056/NEJMsr1606602]
2. Obesity and Cancer Fact Sheet—NCI. Available online: https://www.cancer.gov/about-cancer/causes-prevention/risk/obesity/obesity-fact-sheet (accessed on 16 May 2022).
3. Caan, B.J.; Cespedes Feliciano, E.M.; Kroenke, C.H. The Importance of Body Composition in Explaining the Overweight Paradox in Cancer-Counterpoint. Cancer Res.; 2018; 78, pp. 1906-1912. [DOI: https://dx.doi.org/10.1158/0008-5472.CAN-17-3287]
4. Caan, B.J.; Cespedes Feliciano, E.M.; Prado, C.M.; Alexeeff, S.; Kroenke, C.H.; Bradshaw, P.; Quesenberry, C.P.; Weltzien, E.K.; Castillo, A.L.; Olobatuyi, T.A. et al. Association of Muscle and Adiposity Measured by Computed Tomography With Survival in Patients With Nonmetastatic Breast Cancer. JAMA Oncol.; 2018; 4, pp. 798-804. [DOI: https://dx.doi.org/10.1001/jamaoncol.2018.0137]
5. Yip, C.; Dinkel, C.; Mahajan, A.; Siddique, M.; Cook, G.J.R.; Goh, V. Imaging Body Composition in Cancer Patients: Visceral Obesity, Sarcopenia and Sarcopenic Obesity May Impact on Clinical Outcome. Insights Imaging; 2015; 6, pp. 489-497. [DOI: https://dx.doi.org/10.1007/s13244-015-0414-0]
6. Alavi, D.H.; Henriksen, H.B.; Lauritzen, P.M.; Kværner, A.S.; Sakinis, T.; Langleite, T.M.; Henriksen, C.; Bøhn, S.K.; Paur, I.; Wiedswang, G. et al. Quantification of Adipose Tissues by Dual-Energy X-Ray Absorptiometry and Computed Tomography in Colorectal Cancer Patients. Clin. Nutr. ESPEN; 2021; 43, pp. 360-368. [DOI: https://dx.doi.org/10.1016/j.clnesp.2021.03.022]
7. Arthur, R.S.; Xue, X.; Kamensky, V.; Chlebowski, R.T.; Simon, M.; Luo, J.; Shadyab, A.H.; Neuhouser, M.L.; Banack, H.; Ho, G.Y.F. et al. The Association between DXA-derived Body Fat Measures and Breast Cancer Risk among Postmenopausal Women in the Women’s Health Initiative. Cancer Med.; 2019; 9, pp. 1581-1599. [DOI: https://dx.doi.org/10.1002/cam4.2690]
8. Fukushima, H.; Yokoyama, M.; Nakanishi, Y.; Tobisu, K.; Koga, F. Sarcopenia as a Prognostic Biomarker of Advanced Urothelial Carcinoma. PLoS ONE; 2015; 10, e0115895. [DOI: https://dx.doi.org/10.1371/journal.pone.0115895]
9. Kim, E.Y.; Kim, Y.S.; Park, I.; Ahn, H.K.; Cho, E.K.; Jeong, Y.M. Prognostic Significance of CT-Determined Sarcopenia in Patients with Small-Cell Lung Cancer. J. Thorac. Oncol.; 2015; 10, pp. 1795-1799. [DOI: https://dx.doi.org/10.1097/JTO.0000000000000690]
10. Lee, J.S.; Kim, Y.S.; Kim, E.Y.; Jin, W. Prognostic Significance of CT-Determined Sarcopenia in Patients with Advanced Gastric Cancer. PLoS ONE; 2018; 13, e0202700. [DOI: https://dx.doi.org/10.1371/journal.pone.0202700]
11. Prado, C.; Baracos, V.; McCargar, L.; Reiman, T.; Tonkin, K.; Mackey, J.; Koski, S.; Pituskin, E.; Sawyer, M. Sarcopenia as a Determinant of Chemotherapy Toxicity and Time to Tumor Progression in Metastatic Breast Cancer Patients Receiving Capecitabine Treatment. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res.; 2009; 15, pp. 2920-2926. [DOI: https://dx.doi.org/10.1158/1078-0432.CCR-08-2242]
12. Dalal, S.; Hui, D.; Bidaut, L.; Lem, K.; Del Fabbro, E.; Crane, C.; Reyes-Gibby, C.C.; Bedi, D.; Bruera, E. Relationships among Body Mass Index, Longitudinal Body Composition Alterations, and Survival in Patients with Locally Advanced Pancreatic Cancer Receiving Chemoradiation: A Pilot Study. J. Pain Symptom Manag.; 2012; 44, pp. 181-191. [DOI: https://dx.doi.org/10.1016/j.jpainsymman.2011.09.010]
13. Parsons, H.A.; Baracos, V.E.; Dhillon, N.; Hong, D.S.; Kurzrock, R. Body Composition, Symptoms, and Survival in Advanced Cancer Patients Referred to a Phase I Service. PLoS ONE; 2012; 7, e29330. [DOI: https://dx.doi.org/10.1371/journal.pone.0029330]
14. Bigaard, J.; Frederiksen, K.; Tjønneland, A.; Thomsen, B.L.; Overvad, K.; Heitmann, B.L.; Sørensen, T.I.A. Body Fat and Fat-Free Mass and All-Cause Mortality. Obes. Res.; 2004; 12, pp. 1042-1049. [DOI: https://dx.doi.org/10.1038/oby.2004.131]
15. Toss, F.; Wiklund, P.; Nordström, P.; Nordström, A. Body Composition and Mortality Risk in Later Life. Age Ageing; 2012; 41, pp. 677-681. [DOI: https://dx.doi.org/10.1093/ageing/afs087]
16. Kelly, T.L.; Wilson, K.E.; Heymsfield, S.B. Dual Energy X-Ray Absorptiometry Body Composition Reference Values from NHANES. PLoS ONE; 2009; 4, e7038. [DOI: https://dx.doi.org/10.1371/journal.pone.0007038]
17. Heo, M.; Kabat, G.C.; Gallagher, D.; Heymsfield, S.B.; Rohan, T.E. Optimal Scaling of Weight and Waist Circumference to Height for Maximal Association with DXA-Measured Total Body Fat Mass by Sex, Age and Race/Ethnicity. Int. J. Obes.; 2013; 37, pp. 1154-1160. [DOI: https://dx.doi.org/10.1038/ijo.2012.201]
18. Park, S.-Y.; Wilkens, L.R.; Murphy, S.P.; Monroe, K.R.; Henderson, B.E.; Kolonel, L.N. Body Mass Index and Mortality in an Ethnically Diverse Population: The Multiethnic Cohort Study. Eur. J. Epidemiol.; 2012; 27, pp. 489-497. [DOI: https://dx.doi.org/10.1007/s10654-012-9695-5]
19. Patel, A.V.; Hildebrand, J.S.; Gapstur, S.M. Body Mass Index and All-Cause Mortality in a Large Prospective Cohort of White and Black U.S. Adults. PLoS ONE; 2014; 9, e109153. [DOI: https://dx.doi.org/10.1371/journal.pone.0109153]
20. Cruz-Jentoft, A.J.; Landi, F.; Schneider, S.M.; Zúñiga, C.; Arai, H.; Boirie, Y.; Chen, L.-K.; Fielding, R.A.; Martin, F.C.; Michel, J.-P. et al. Prevalence of and Interventions for Sarcopenia in Ageing Adults: A Systematic Review. Report of the International Sarcopenia Initiative (EWGSOP and IWGS). Age Ageing; 2014; 43, pp. 748-759. [DOI: https://dx.doi.org/10.1093/ageing/afu115]
21. Goodman, M.J.; Ghate, S.R.; Mavros, P.; Sen, S.; Marcus, R.L.; Joy, E.; Brixner, D.I. Development of a Practical Screening Tool to Predict Low Muscle Mass Using NHANES 1999–2004. J. Cachexia Sarcopenia Muscle; 2013; 4, pp. 187-197. [DOI: https://dx.doi.org/10.1007/s13539-013-0107-9]
22. Baumgartner, R.N.; Koehler, K.M.; Gallagher, D.; Romero, L.; Heymsfield, S.B.; Ross, R.R.; Garry, P.J.; Lindeman, R.D. Epidemiology of Sarcopenia among the Elderly in New Mexico. Am. J. Epidemiol.; 1998; 147, pp. 755-763. [DOI: https://dx.doi.org/10.1093/oxfordjournals.aje.a009520]
23. Padwal, R.; Leslie, W.D.; Lix, L.M.; Majumdar, S.R. Relationship Among Body Fat Percentage, Body Mass Index, and All-Cause Mortality: A Cohort Study. Ann. Intern. Med.; 2016; 164, pp. 532-541. [DOI: https://dx.doi.org/10.7326/M15-1181]
24. Zong, G.; Zhang, Z.; Yang, Q.; Wu, H.; Hu, F.B.; Sun, Q. Total and Regional Adiposity Measured by Dual-Energy X-Ray Absorptiometry and Mortality in NHANES 1999–2006. Obesity; 2016; 24, pp. 2414-2421. [DOI: https://dx.doi.org/10.1002/oby.21659]
25. Allison, D.B.; Zhu, S.K.; Plankey, M.; Faith, M.S.; Heo, M. Differential Associations of Body Mass Index and Adiposity with All-Cause Mortality among Men in the First and Second National Health and Nutrition Examination Surveys (NHANES I and NHANES II) Follow-up Studies. Int. J. Obes.; 2002; 26, pp. 410-416. [DOI: https://dx.doi.org/10.1038/sj.ijo.0801925]
26. Lee, D.H.; Keum, N.; Hu, F.B.; Orav, E.J.; Rimm, E.B.; Willett, W.C.; Giovannucci, E.L. Predicted Lean Body Mass, Fat Mass, and All Cause and Cause Specific Mortality in Men: Prospective US Cohort Study. BMJ; 2018; 362, k2575. [DOI: https://dx.doi.org/10.1136/bmj.k2575]
27. Heitmann, B.L.; Erikson, H.; Ellsinger, B.M.; Mikkelsen, K.L.; Larsson, B. Mortality Associated with Body Fat, Fat-Free Mass and Body Mass Index among 60-Year-Old Swedish Men-a 22-Year Follow-up. The Study of Men Born in 1913. Int. J. Obes. Relat. Metab. Disord. J. Int. Assoc. Study Obes.; 2000; 24, pp. 33-37. [DOI: https://dx.doi.org/10.1038/sj.ijo.0801082]
28. Rolland, Y.; Gallini, A.; Cristini, C.; Schott, A.-M.; Blain, H.; Beauchet, O.; Cesari, M.; Lauwers-Cances, V. Body-Composition Predictors of Mortality in Women Aged ≥75 y: Data from a Large Population-Based Cohort Study with a 17-y Follow-Up. Am. J. Clin. Nutr.; 2014; 100, pp. 1352-1360. [DOI: https://dx.doi.org/10.3945/ajcn.114.086728]
29. Auyeung, T.W.; Lee, J.S.W.; Leung, J.; Kwok, T.; Leung, P.C.; Woo, J. Survival in Older Men May Benefit From Being Slightly Overweight and Centrally Obese—A 5-Year Follow-up Study in 4000 Older Adults Using DXA. J. Gerontol. Ser. A Biol. Sci. Med. Sci.; 2010; 65A, pp. 99-104. [DOI: https://dx.doi.org/10.1093/gerona/glp099]
30. Cesari, M.; Pahor, M.; Lauretani, F.; Zamboni, V.; Bandinelli, S.; Bernabei, R.; Guralnik, J.M.; Ferrucci, L. Skeletal Muscle and Mortality Results from the InCHIANTI Study. J. Gerontol. Ser. A Biol. Sci. Med. Sci.; 2009; 64A, pp. 377-384. [DOI: https://dx.doi.org/10.1093/gerona/gln031]
31. Wannamethee, S.G.; Shaper, A.G.; Lennon, L.; Whincup, P.H. Decreased Muscle Mass and Increased Central Adiposity Are Independently Related to Mortality in Older Men. Am. J. Clin. Nutr.; 2007; 86, pp. 1339-1346. [DOI: https://dx.doi.org/10.1093/ajcn/86.5.1339]
32. Genton, L.; Graf, C.E.; Karsegard, V.L.; Kyle, U.G.; Pichard, C. Low Fat-Free Mass as a Marker of Mortality in Community-Dwelling Healthy Elderly Subjects†. Age Ageing; 2013; 42, pp. 33-39. [DOI: https://dx.doi.org/10.1093/ageing/afs091]
33. Katzmarzyk, P.T.; Brown, J.C.; Yang, S.; Mire, E.F.; Wu, X.-C.; Miele, L.; Ochoa, A.C.; Zabaleta, J. Association of Abdominal Visceral Adiposity and Total Fat Mass with Cancer Incidence and Mortality in White and Black Adults. Cancer Epidemiol. Biomark. Prev.; 2022; 31, pp. 1532-1538. [DOI: https://dx.doi.org/10.1158/1055-9965.EPI-22-0207]
34. Velho, S.; Costa Santos, M.P.; Cunha, C.; Agostinho, L.; Cruz, R.; Costa, F.; Garcia, M.; Oliveira, P.; Maio, R.; Baracos, V.E. et al. Body Composition Influences Post-Operative Complications and 90-Day and Overall Survival in Pancreatic Surgery Patients. GE Port. J. Gastroenterol.; 2021; 28, pp. 13-25. [DOI: https://dx.doi.org/10.1159/000507206]
35. Lopez, P.; Newton, R.U.; Taaffe, D.R.; Singh, F.; Buffart, L.M.; Spry, N.; Tang, C.; Saad, F.; Galvão, D.A. Associations of Fat and Muscle Mass with Overall Survival in Men with Prostate Cancer: A Systematic Review with Meta-Analysis. Prostate Cancer Prostatic Dis.; 2021; 25, pp. 615-626. [DOI: https://dx.doi.org/10.1038/s41391-021-00442-0]
36. Cheng, E.; Kirley, J.; Cespedes Feliciano, E.M.; Caan, B.J. Adiposity and Cancer Survival: A Systematic Review and Meta-Analysis. Cancer Causes Control CCC; 2022; 33, pp. 1219-1246. [DOI: https://dx.doi.org/10.1007/s10552-022-01613-7]
37. Kuk, J.L.; Ardern, C.I. Influence of Age on the Association between Various Measures of Obesity and All-Cause Mortality. J. Am. Geriatr. Soc.; 2009; 57, pp. 2077-2084. [DOI: https://dx.doi.org/10.1111/j.1532-5415.2009.02486.x]
38. Metter, E.J.; Talbot, L.A.; Schrager, M.; Conwit, R. Skeletal Muscle Strength as a Predictor of All-Cause Mortality in Healthy Men. J. Gerontol. Ser. A Biol. Sci. Med. Sci.; 2002; 57, pp. B359-B365. [DOI: https://dx.doi.org/10.1093/gerona/57.10.B359]
39. Newman, A.B.; Kupelian, V.; Visser, M.; Simonsick, E.M.; Goodpaster, B.H.; Kritchevsky, S.B.; Tylavsky, F.A.; Rubin, S.M.; Harris, T.B. Strength, but Not Muscle Mass, Is Associated with Mortality in the Health, Aging and Body Composition Study Cohort. J. Gerontol. Ser. A Biol. Sci. Med. Sci.; 2006; 61, pp. 72-77. [DOI: https://dx.doi.org/10.1093/gerona/61.1.72]
40. Simpson, J.A.; MacInnis, R.J.; Peeters, A.; Hopper, J.L.; Giles, G.G.; English, D.R. A Comparison of Adiposity Measures as Predictors of All-Cause Mortality: The Melbourne Collaborative Cohort Study. Obesity; 2007; 15, pp. 994-1003. [DOI: https://dx.doi.org/10.1038/oby.2007.622]
41. Adams, K.F.; Schatzkin, A.; Harris, T.B.; Kipnis, V.; Mouw, T.; Ballard-Barbash, R.; Hollenbeck, A.; Leitzmann, M.F. Overweight, Obesity, and Mortality in a Large Prospective Cohort of Persons 50 to 71 Years Old. N. Engl. J. Med.; 2006; 355, pp. 763-778. [DOI: https://dx.doi.org/10.1056/NEJMoa055643]
42. McTigue, K.; Larson, J.C.; Valoski, A.; Burke, G.; Kotchen, J.; Lewis, C.E.; Stefanick, M.L.; Van Horn, L.; Kuller, L. Mortality and Cardiac and Vascular Outcomes in Extremely Obese Women. JAMA; 2006; 296, pp. 79-86. [DOI: https://dx.doi.org/10.1001/jama.296.1.79]
43. Pischon, T.; Boeing, H.; Hoffmann, K.; Bergmann, M.; Schulze, M.B.; Overvad, K.; van der Schouw, Y.T.; Spencer, E.; Moons, K.G.M.; Tjønneland, A. et al. General and Abdominal Adiposity and Risk of Death in Europe. N. Engl. J. Med.; 2008; 359, pp. 2105-2120. [DOI: https://dx.doi.org/10.1056/NEJMoa0801891]
44. Freedman, D.M.; Ron, E.; Ballard-Barbash, R.; Doody, M.M.; Linet, M.S. Body Mass Index and All-Cause Mortality in a Nationwide US Cohort. Int. J. Obes.; 2006; 30, pp. 822-829. [DOI: https://dx.doi.org/10.1038/sj.ijo.0803193]
45. Gelber, R.P.; Kurth, T.; Manson, J.E.; Buring, J.E.; Gaziano, J.M. Body Mass Index and Mortality in Men: Evaluating the Shape of the Association. Int. J. Obes.; 2007; 31, pp. 1240-1247. [DOI: https://dx.doi.org/10.1038/sj.ijo.0803564]
46. Koster, A.; Leitzmann, M.F.; Schatzkin, A.; Adams, K.F.; van Eijk, J.T.M.; Hollenbeck, A.R.; Harris, T.B. The Combined Relations of Adiposity and Smoking on Mortality. Am. J. Clin. Nutr.; 2008; 88, pp. 1206-1212. [DOI: https://dx.doi.org/10.3945/ajcn.2008.26298]
47. Pednekar, M.S.; Hakama, M.; Hebert, J.R.; Gupta, P.C. Association of Body Mass Index with All-Cause and Cause-Specific Mortality: Findings from a Prospective Cohort Study in Mumbai (Bombay), India. Int. J. Epidemiol.; 2008; 37, pp. 524-535. [DOI: https://dx.doi.org/10.1093/ije/dyn001]
48. Ringbäck Weitoft, G.; Eliasson, M.; Rosén, M. Underweight, Overweight and Obesity as Risk Factors for Mortality and Hospitalization. Scand. J. Public Health; 2008; 36, pp. 169-176. [DOI: https://dx.doi.org/10.1177/1403494807085080]
49. Boggs, D.A.; Rosenberg, L.; Cozier, Y.C.; Wise, L.A.; Coogan, P.F.; Ruiz-Narvaez, E.A.; Palmer, J.R. General and Abdominal Obesity and Risk of Death among Black Women. N. Engl. J. Med.; 2011; 365, pp. 901-908. [DOI: https://dx.doi.org/10.1056/NEJMoa1104119]
50. Stevens, J. Obesity and Mortality in African Americans. Nutr. Rev.; 2000; 58, pp. 346-353. [DOI: https://dx.doi.org/10.1111/j.1753-4887.2000.tb01832.x]
51. Reis, J.P.; Araneta, M.R.; Wingard, D.L.; Macera, C.A.; Lindsay, S.P.; Marshall, S.J. Overall Obesity and Abdominal Adiposity as Predictors of Mortality in u.s. White and Black Adults. Ann. Epidemiol.; 2009; 19, pp. 134-142. [DOI: https://dx.doi.org/10.1016/j.annepidem.2008.10.008]
52. Calle, E.E.; Thun, M.J.; Petrelli, J.M.; Rodriguez, C.; Heath, C.W. Body-Mass Index and Mortality in a Prospective Cohort of U.S. Adults. N. Engl. J. Med.; 1999; 341, pp. 1097-1105. [DOI: https://dx.doi.org/10.1056/NEJM199910073411501] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10511607]
53. Ziolkowski, S.L.; Long, J.; Baker, J.F.; Chertow, G.M.; Leonard, M.B. Relative Sarcopenia and Mortality and the Modifying Effects of Chronic Kidney Disease and Adiposity. J. Cachexia Sarcopenia Muscle; 2019; 10, pp. 338-346. [DOI: https://dx.doi.org/10.1002/jcsm.12396] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30784237]
54. Hurst, L.; Stafford, M.; Cooper, R.; Hardy, R.; Richards, M.; Kuh, D. Lifetime Socioeconomic Inequalities in Physical and Cognitive Aging. Am. J. Public Health; 2013; 103, pp. 1641-1648. [DOI: https://dx.doi.org/10.2105/AJPH.2013.301240] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23865666]
55. Fanelli Kuczmarski, M.; Mason, M.A.; Beydoun, M.A.; Allegro, D.; Zonderman, A.B.; Evans, M.K. Dietary Patterns and Sarcopenia in an Urban African American and White Population in the United States. J. Nutr. Gerontol. Geriatr.; 2013; 32, pp. 291-316. [DOI: https://dx.doi.org/10.1080/21551197.2013.840255] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24224938]
56. Chaffer, C.L.; Weinberg, R.A. A Perspective on Cancer Cell Metastasis. Science; 2011; 331, pp. 1559-1564. [DOI: https://dx.doi.org/10.1126/science.1203543]
57. Pak, S.; Kim, M.S.; Park, E.Y.; Kim, S.H.; Lee, K.H.; Joung, J.Y. Association of Body Composition With Survival and Treatment Efficacy in Castration-Resistant Prostate Cancer. Front. Oncol.; 2020; 10, 558. [DOI: https://dx.doi.org/10.3389/fonc.2020.00558]
58. Ying, P.; Jin, W.; Wu, X.; Cai, W. Association between CT-Quantified Body Composition and Recurrence, Survival in Nonmetastasis Colorectal Cancer Patients Underwent Regular Chemotherapy after Surgery. BioMed Res. Int.; 2021; 2021, 6657566. [DOI: https://dx.doi.org/10.1155/2021/6657566]
59. Han, J.S.; Ryu, H.; Park, I.J.; Kim, K.W.; Shin, Y.; Kim, S.O.; Lim, S.-B.; Kim, C.W.; Yoon, Y.S.; Lee, J.L. et al. Association of Body Composition with Long-Term Survival in Non-Metastatic Rectal Cancer Patients. Cancer Res. Treat.; 2020; 52, pp. 563-572. [DOI: https://dx.doi.org/10.4143/crt.2019.249]
60. Cespedes Feliciano, E.M.; Kroenke, C.H.; Meyerhardt, J.A.; Prado, C.M.; Bradshaw, P.T.; Kwan, M.L.; Xiao, J.; Alexeeff, S.; Corley, D.; Weltzien, E. et al. Association of Systemic Inflammation and Sarcopenia With Survival in Nonmetastatic Colorectal Cancer: Results From the C SCANS Study. JAMA Oncol.; 2017; 3, e172319. [DOI: https://dx.doi.org/10.1001/jamaoncol.2017.2319]
61. Deluche, E.; Leobon, S.; Desport, J.C.; Venat-Bouvet, L.; Usseglio, J.; Tubiana-Mathieu, N. Impact of Body Composition on Outcome in Patients with Early Breast Cancer. Support. Care Cancer; 2018; 26, pp. 861-868. [DOI: https://dx.doi.org/10.1007/s00520-017-3902-6]
62. Pak, S.; Park, S.Y.; Shin, T.J.; You, D.; Jeong, I.G.; Hong, J.H.; Kim, C.-S.; Ahn, H. Association of Muscle Mass with Survival after Radical Prostatectomy in Patients with Prostate Cancer. J. Urol.; 2019; 202, pp. 525-532. [DOI: https://dx.doi.org/10.1097/JU.0000000000000249]
63. Narici, M.V.; Maffulli, N. Sarcopenia: Characteristics, Mechanisms and Functional Significance. Br. Med. Bull.; 2010; 95, pp. 139-159. [DOI: https://dx.doi.org/10.1093/bmb/ldq008]
64. Avgerinou, C. Sarcopenia: Why It Matters in General Practice. Br. J. Gen. Pract.; 2020; 70, pp. 200-201. [DOI: https://dx.doi.org/10.3399/bjgp20X709253]
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Karanth, Shama D 2
; Wheeler, Meghann 1 ; Yang, Danting 1
; Washington, Caretia 1
; Young-Rock, Hong 3
; Manini, Todd M 4 ; Fabregas, Jesus C 5 ; Ting-Yuan, David Cheng 6 ; Braithwaite, Dejana 7
1 Department of Epidemiology, University of Florida, 2004 Mowry Rd., Gainesville, FL 32610, USA
2 University of Florida Health Cancer Center, University of Florida, 2004 Mowry Rd., Gainesville, FL 32610, USA; Aging & Geriatric Research, Institute on Aging, 2004 Mowry Rd., Gainesville, FL 32610, USA
3 Department of Health Services Research, Management, & Policy, 1225 Center Dr., Gainesville, FL 32610, USA
4 Department Health Outcomes & Biomedical Informatics, Institute on Aging, 2004 Mowry Rd., Gainesville, FL 32611, USA
5 Division of Hematology & Oncology, College of Medicine, University of Florida, 2000 SW Archer Rd., Gainesville, FL 32608, USA
6 Division of Cancer Prevention and Control, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA
7 Department of Epidemiology, University of Florida, 2004 Mowry Rd., Gainesville, FL 32610, USA; University of Florida Health Cancer Center, University of Florida, 2004 Mowry Rd., Gainesville, FL 32610, USA; Department of Surgery, University of Florida, 1600 SW Archer Rd., Gainesville, FL 32608, USA