The American Cancer Society (ACS) publishes the Diet and Physical Activity Guideline to serve as a foundation for its communication, policy, and community strategies and, ultimately, to affect ...dietary and physical activity patterns among Americans. This guideline is developed by a national panel of experts in cancer research, prevention, epidemiology, public health, and policy, and reflects the most current scientific evidence related to dietary and activity patterns and cancer risk. The ACS guideline focuses on recommendations for individual choices regarding diet and physical activity patterns, but those choices occur within a community context that either facilitates or creates barriers to healthy behaviors. Therefore, this committee presents recommendations for community action to accompany the 4 recommendations for individual choices to reduce cancer risk. These recommendations for community action recognize that a supportive social and physical environment is indispensable if individuals at all levels of society are to have genuine opportunities to choose healthy behaviors. This 2020 ACS guideline is consistent with guidelines from the American Heart Association and the American Diabetes Association for the prevention of coronary heart disease and diabetes as well as for general health promotion, as defined by the 2015 to 2020 Dietary Guidelines for Americans and the 2018 Physical Activity Guidelines for Americans.
Sarcopenia in the Older Adult With Cancer Williams, Grant R; Dunne, Richard F; Giri, Smith ...
Journal of clinical oncology,
07/2021, Volume:
39, Issue:
19
Journal Article
Body composition may partially explain the U-shaped association between body mass index (BMI) and colorectal cancer survival.
Muscle and adiposity at colorectal cancer diagnosis and survival were ...examined in a retrospective cohort using Kaplan-Meier curves, multivariable Cox regression, and restricted cubic splines in 3,262 early-stage (I-III) male (50%) and female (50%) patients. Sarcopenia was defined using optimal stratification and sex- and BMI-specific cut points. High adiposity was defined as the highest tertile of sex-specific total adipose tissue (TAT). Primary outcomes were overall mortality and colorectal cancer-specific mortality (CRCsM).
Slightly over 42% patients were sarcopenic. During 5.8 years of follow-up, 788 deaths occurred, including 433 from colorectal cancer. Sarcopenic patients had a 27% HR, 1.27; 95% confidence interval (CI), 1.09-1.48 higher risk of overall mortality than those who were not sarcopenic. Females with both low muscle and high adiposity had a 64% higher risk of overall mortality (HR, 1.64; 95% CI, 1.05-2.57) than females with adequate muscle and lower adiposity. The lowest risk of overall mortality was seen in patients with a BMI between 25 and <30 kg/m
, a range associated with the greatest number of patients (58.6%) who were not at increased risk of overall mortality due to either low muscle or high adiposity.
Sarcopenia is prevalent among patients with non-metastatic colorectal cancer, and should, along with adiposity be a standard oncological marker.
Our findings suggest a biologic explanation for the obesity paradox in colorectal cancer and refute the notion that the association between overweight and lower mortality is due solely to methodologic biases.
.
Although higher body mass index (BMI) increases the incidence of many cancers, BMI can also exhibit a null or U-shaped relationship with survival among patients with existing disease; this ...association of higher BMI with improved survival is termed the obesity paradox. This review discusses possible explanations for the obesity paradox, the prevalence and consequences of low muscle mass in cancer patients, and future research directions. It is unlikely that methodological biases, such as reverse causality or confounding, fully explain the obesity paradox. Rather, up to a point, higher BMI may truly be associated with longer survival in cancer patients. This is due, in part, to the limitations of BMI, which scales weight to height without delineating adipose tissue distribution or distinguishing between adipose and muscle tissue. Thus, cancer patients with higher BMIs often have higher levels of protective muscle. We assert that more precise measures of body composition are required to clarify the relationship of body size to cancer outcomes, inform clinical decision-making, and help tailor lifestyle interventions.
Estimating Kinship in Admixed Populations Thornton, Timothy; Tang, Hua; Hoffmann, Thomas J. ...
American journal of human genetics,
07/2012, Volume:
91, Issue:
1
Journal Article
Peer reviewed
Open access
Genome-wide association studies (GWASs) are commonly used for the mapping of genetic loci that influence complex traits. A problem that is often encountered in both population-based and family-based ...GWASs is that of identifying cryptic relatedness and population stratification because it is well known that failure to appropriately account for both pedigree and population structure can lead to spurious association. A number of methods have been proposed for identifying relatives in samples from homogeneous populations. A strong assumption of population homogeneity, however, is often untenable, and many GWASs include samples from structured populations. Here, we consider the problem of estimating relatedness in structured populations with admixed ancestry. We propose a method, REAP (relatedness estimation in admixed populations), for robust estimation of identity by descent (IBD)-sharing probabilities and kinship coefficients in admixed populations. REAP appropriately accounts for population structure and ancestry-related assortative mating by using individual-specific allele frequencies at SNPs that are calculated on the basis of ancestry derived from whole-genome analysis. In simulation studies with related individuals and admixture from highly divergent populations, we demonstrate that REAP gives accurate IBD-sharing probabilities and kinship coefficients. We apply REAP to the Mexican Americans in Los Angeles, California (MXL) population sample of release 3 of phase III of the International Haplotype Map Project; in this sample, we identify third- and fourth-degree relatives who have not previously been reported. We also apply REAP to the African American and Hispanic samples from the Women's Health Initiative SNP Health Association Resource (WHI-SHARe) study, in which hundreds of pairs of cryptically related individuals have been identified.
To determine whether bariatric surgery is associated with a lower risk of cancer.
Obesity is strongly associated with many types of cancer. Few studies have examined the relationship between ...bariatric surgery and cancer risk.
We conducted a retrospective cohort study of patients undergoing bariatric surgery between 2005 and 2012 with follow-up through 2014 using data from a large integrated health insurance and care delivery systems with 5 study sites. The study included 22,198 subjects who had bariatric surgery and 66,427 nonsurgical subjects matched on sex, age, study site, body mass index, and Elixhauser comorbidity index. Multivariable Cox proportional-hazards models were used to examine incident cancer up to 10 years after bariatric surgery compared to the matched nonsurgical patients.
After a mean follow-up of 3.5 years, we identified 2543 incident cancers. Patients undergoing bariatric surgery had a 33% lower hazard of developing any cancer during follow-up hazard ratio (HR) 0.67, 95% confidence interval (CI) 0.60, 0.74, P < 0.001) compared with matched patients with severe obesity who did not undergo bariatric surgery, and results were even stronger when the outcome was restricted to obesity-associated cancers (HR 0.59, 95% CI 0.51, 0.69, P < 0.001). Among the obesity-associated cancers, the risk of postmenopausal breast cancer (HR 0.58, 95% CI 0.44, 0.77, P < 0.001), colon cancer (HR 0.59, 95% CI 0.36, 0.97, P = 0.04), endometrial cancer (HR 0.50, 95% CI 0.37, 0.67, P < 0.001), and pancreatic cancer (HR 0.46, 95% CI 0.22, 0.97, P = 0.04) was each statistically significantly lower among those who had undergone bariatric surgery compared with matched nonsurgical patients.
In this large, multisite cohort of patients with severe obesity, bariatric surgery was associated with a lower risk of incident cancer, particularly obesity-associated cancers, such as postmenopausal breast cancer, endometrial cancer, and colon cancer. More research is needed to clarify the specific mechanisms through which bariatric surgery lowers cancer risk.
There is growing interest from the oncology community to understand how body composition measures can be used to improve the delivery of clinical care for the 18.1 million individuals diagnosed with ...cancer annually. Methods that distinguish muscle from subcutaneous and visceral adipose tissue, such as computed tomography (CT), may offer new insights of important risk factors and improved prognostication of outcomes over alternative measures such as body mass index. In a meta‐analysis of 38 studies, low muscle area assessed from clinically acquired CT was observed in 27.7% of patients with cancer and associated with poorer overall survival hazard ratio: 1.44, 95% CI: 1.32–1.56. Therapeutic interventions such as lifestyle and pharmacotherapy that modify all aspects of body composition and reduce the incidence of poor clinical outcomes are needed in patients with cancer. In a meta‐analysis of six randomized trials, resistance training exercise increased lean body mass assessed from dual‐energy X‐ray absorptiometry mean difference (MD): +1.07 kg, 95% CI: 0.76–1.37; P < 0.001 and walking distance MD: +143 m, 95% CI: 70–216; P < 0.001 compared with usual care control in patients with non‐metastatic cancer. In a meta‐analysis of five randomized trials, anamorelin (a ghrelin agonist) significantly increased lean body mass MD: +1.10 kg, 95% CI: 0.35–1.85; P = 0.004 but did not improve handgrip strength MD: 0.52 kg, 95% CI: −0.09–1.13; P = 0.09 or overall survival compared with placebo HR: 0.99, 95% CI: 0.85–1.14; P = 0.84 in patients with advanced or metastatic cancer. Early screening to identify individuals with occult muscle loss, combined with multimodal interventions that include lifestyle therapy with resistance exercise training and dietary supplementation combined with pharmacotherapy, may be necessary to provide a sufficient stimulus to prevent or slow the cascade of tissue wasting. Rapid, cost‐efficient, and feasible methods to quantify muscle and adipose tissue distribution are needed if body composition assessment is to be integrated into large‐scale clinical workflows. Fully automated analysis of body composition from clinically acquired imaging is one example. The study of body composition is one of the most provocative areas in oncology that offers tremendous promise to help patients with cancer live longer and healthier lives.
Disruption of systemic homeostasis by either chronic or acute stressors, such as obesity
or surgery
, alters cancer pathogenesis. Patients with cancer, particularly those with breast cancer, can be ...at increased risk of cardiovascular disease due to treatment toxicity and changes in lifestyle behaviors
. While elevated risk and incidence of cardiovascular events in breast cancer is well established, whether such events impact cancer pathogenesis is not known. Here we show that myocardial infarction (MI) accelerates breast cancer outgrowth and cancer-specific mortality in mice and humans. In mouse models of breast cancer, MI epigenetically reprogrammed Ly6C
monocytes in the bone marrow reservoir to an immunosuppressive phenotype that was maintained at the transcriptional level in monocytes in both the circulation and tumor. In parallel, MI increased circulating Ly6C
monocyte levels and recruitment to tumors and depletion of these cells abrogated MI-induced tumor growth. Furthermore, patients with early-stage breast cancer who experienced cardiovascular events after cancer diagnosis had increased risk of recurrence and cancer-specific death. These preclinical and clinical results demonstrate that MI induces alterations in systemic homeostasis, triggering cross-disease communication that accelerates breast cancer.
•A new convolutional neural network is presented that takes as input an axial slice from a CT image at L3 or T4 level and generates the muscle segmentation mask of the image in almost real time (it ...takes less than one second (∼200 ms) for the trained network to generate the muscle mask).•The performance of the network on three large datasets is evaluated and demonstrates high Jaccard scores in the range of 0.96–0.98 on these datasets.•We validated the model's robustness by reporting the performance of the model on three different (and unseen) datasets generated by different CT devices and data acquisition settings, males and females, with a variety of muscle tissue shape and form, in various cancers attesting to the generalizability of the result.•In total more than 9000 L3 images were used for investigating (train/test) the proposed model. This is considerably higher than the number of images used for validating the methods in other papers in the literature attesting to robustness of the results.•The model trained on the large set of L3 is fine-tuned for T4 muscle segmentation. The model's performance is investigated with various experiments considering different ratio of test and training images and we find that even with a small number of images at the T4 level, having a model trained at the L3 level provides a very strong initialization to develop an accurate model for the T4 level. This indicates future generalizability to other axial locations in the CT image stack.
In diseases such as cancer, patients suffer from degenerative loss of skeletal muscle (cachexia). Muscle wasting and loss of muscle function/performance (sarcopenia) can also occur during advanced aging. Assessing skeletal muscle mass in sarcopenia and cachexia is therefore of clinical interest for risk stratification. In comparison with fat, body fluids and bone, quantifying the skeletal muscle mass is more challenging. Computed tomography (CT) is one of the gold standard techniques for cancer diagnostics and analysis of progression, and therefore a valuable source of imaging for in vivo quantification of skeletal muscle mass. In this paper, we design a novel deep neural network-based algorithm for the automated segmentation of skeletal muscle in axial CT images at the third lumbar (L3) and the fourth thoracic (T4) levels. A two-branch network with two training steps is investigated. The network's performance is evaluated for three trained models on separate datasets. These datasets were generated by different CT devices and data acquisition settings. To ensure the model's robustness, each trained model was tested on all three available test sets. Errors and the effect of labeling protocol in these cases were analyzed and reported. The best performance of the proposed algorithm was achieved on 1327 L3 test samples with an overlap Jaccard score of 98% and sensitivity and specificity greater than 99%.
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