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Background: Energy requirement assessment is a cornerstone for nutrition practice. The extent to which total energy expenditure (TEE; indicator of energy requirements) has been measured in adults with chronic diseases has not been explored., Objectives: This systematic review aimed to characterize evidence on TEE among individuals with chronic diseases and describe TEE across chronic diseases and in comparison to controls without a chronic disease., Methods: A literature search using terms related to doubly labeled water and TEE was conducted in PubMed, MEDLINE, Web of Science, and Embase. Eligible articles included those that measured TEE using doubly labeled water in adults with a major chronic disease. Methodological quality was determined using the Academy of Nutrition and Dietetics quality criteria checklist. Sample size-weighted TEE was calculated in each chronic disease subgroup., Results: Fifty studies were included, of which 15 had a control group. Median sample size was 20 participants, and approximately half of studies were published over 10 y ago. Thirty-five (70%) studies reported resting energy expenditure, and approximately half (k = 26) reported physical activity level. Methodological quality was neutral (k = 25) or positive (k = 23) for most studies. TEE among individual studies ranged from 934 to 3274 kcal/d. Mean weighted TEE was lowest among gastrointestinal (1786 kcal/d) and neurologic (2104 kcal/d) subgroups and highest among cancer (2903 kcal/d), endocrine (2661 kcal/d), and autoimmune (2625 kcal/d) subgroups. Excluding 1 article in cancer survivors resulted in a low TEE in the cancer subgroup (2112 kcal/d). Most studies with a control group reported no differences in TEE between controls and patients; however, only 1 study was powered for between-group comparisons., Conclusions: Energy requirements vary across chronic diseases, although there is insufficient evidence to suggest that TEE in patients with chronic disease is different than that among controls. Further research is needed to inform energy requirement recommendations that consider chronic disease. This review was registered at PROSPERO as CRD42022336500 (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=336500)., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

We tested whether spontaneous physical activity (SPA) from accelerometers could be used in a whole room calorimeter to estimate thermic effect of food (TEF). Eleven healthy participants ( n = 7 females; age: 27 ± 4 yr; body mass index: 22.8 ± 2.6 kg/m 2 ) completed two 23-h visits in randomized order: one "fed" with meals provided and one "fasted" with no food. SPA was measured by ActivPAL and Actigraph accelerometers. Criterion TEF was calculated as the difference in total daily energy expenditure (TDEE) between fed and fasted visits and compared with three methods of estimating TEF: 1 ) SPA-adjusted TEF (adjTEF)-difference in TDEE without SPA between visits, 2 ) Wakeful TEF-difference in energy expenditure obtained from linear regression and basal metabolic rate during waking hours, 3 ) 24-h TEF-increase in TDEE above SPA and sleeping metabolic rate. Criterion TEF was 9.4 ± 4.5% of TDEE. AdjTEF (difference in estimated vs. criterion TEF: activPAL: -0.3 ± 3.3%; Actigraph: -1.8 ± 8.0%) and wakeful TEF (activPAL: -0.9 ± 6.1%; Actigraph: -2.8 ± 7.6%) derived from both accelerometers did not differ from criterion TEF (all P > 0.05). ActivPAL-derived 24-h TEF overestimated TEF (6.8 ± 5.4%, P = 0.002), whereas Actigraph-derived 24-h TEF was not significantly different (4.3 ± 9.4%, P = 0.156). TEF estimations using activPAL tended to show better individual-level agreement (i.e., smaller coefficients of variation). Both accelerometers can be used to estimate TEF in a whole room calorimeter; wakeful TEF using activPAL is the most viable option given strong group-level accuracy and reasonable individual agreement. NEW & NOTEWORTHY Two research-grade accelerometers can effectively estimate spontaneous physical activity and improve the estimation of thermic effect of food (TEF) in whole room calorimeters. The activPAL demonstrates strong group-level accuracy and reasonable individual-level agreement in estimating wakeful TEF, whereas a hip-worn Actigraph is an acceptable approach for estimating 24-h TEF. These results highlight the promising potential of accelerometers in advancing energy balance research by improving the assessment of TEF within whole room calorimeters.

Purpose: Metabolic diseases such as diabetes mellitus may play a role in the development and progression of prostate cancer (PC); however, this association remains to be explored in the context of specific PC stages. The objective of this study was to systematically review the evidence for an association between diabetes and overall, early, or advanced PC risk., Materials and Methods: A systematic review with meta-analysis was performed (MEDLINE, EMBASE, and CINAHL) from inception until September 2023. Cohort and case-control studies that assessed PC risk in adult males (≥18 years) associated with type 2 diabetes mellitus or diabetes (if there was no distinction between diabetes type) were included. The Newcastle-Ottawa Scale (NOS) was used to assess study bias; those with NOS<7 were excluded. Evidence certainty was assessed with the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) method., Results: Thirty-four studies (n=26 cohorts and n=8 case-controls) were included. Of these, 32 assessed diabetes and all PC stages combined, 12 included early PC stages, and 15 included advanced PC stages. Our meta-analysis showed diabetes had a protective effect against early PC development (n=11, risk ratio [RR]=0.71; 95% confidence interval [CI]=0.61-0.83, I²=84%) but no association was found for combined (n=21, RR=0.95; 95% CI=0.79-1.13, I²=99%) or advanced PC stages (n=15, RR=0.96; 95% CI=0.77-1.18, I²=98%) at diagnosis. According to GRADE, the evidence certainty was very low., Conclusions: Diabetes may be protective against early PC stages, yet evidence linking diabetes to risk across all stages, and advanced PC specifically, is less conclusive. High heterogeneity may partially explain discrepancy in findings and was mostly associated with study design, method used for PC diagnosis, and risk measures. Our results may aid risk stratification of males with diabetes and inform new approaches for PC screening in this group, especially considering the reduced sensitivity of prostate-specific antigen values for those with diabetes., Competing Interests: APP, BRS, FTV, LFMF, SAP, JDL, MLM, PJR, JEV, FMS have nothing to disclose. CMP reports receiving unrelated honoraria and/or paid consultancy from Abbott Nutrition, Nutricia, Nestle Health Science, Pfizer, and AMRA medical but she made no influence on this work in relation with the companies or their products., (Copyright © 2024 Korean Society for Sexual Medicine and Andrology.)

Background & Aims: Brief screening questionnaires can identify 'at risk' behaviors in clinical settings. However, there is currently no screener for dietary intake specifically developed using foods associated with body weight change and increased risk for multiple chronic conditions and diseases., Methods: We developed a novel brief dietary screener, the 24-Hour Food Frequency Assessment Screening Tool Questionnaire (FAST24), to identify intake of foods associated with weight change. University students completed the FAST24 and the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) at two time points to assess acceptability and determine preliminary criterion validity against food categories from the United States Department of Agriculture (USDA) Food Patterns Equivalents Database (FPED)., Results: 202 individuals (age 20.4 ± 3.6 years; 65.7% females) completed the FAST24 in an average time of 2 min compared to 24 min for the ASA24. Over half of the food items from the FAST24 were matched to, and correlated with, standard USDA food pattern components (r's ranging from .15 to .58, p's < .05). Food items from the dietary data from the FAST24 were also highly correlated with the more intensive ASA24 application (r's ranging from .23 to .82, p's < .01), and were less time-consuming and burdensome to complete (p's < .0001)., Conclusions: Findings support the continued refinement of the FAST24 as a rapid, valid primary care assessment tool for measuring USDA dietary intake patterns. Use of a short, simple screener such as the FAST24 has the potential for integration into large healthcare delivery settings to help establish a baseline for promoting relative behavior changes critical for long-term health and well-being., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Published by Elsevier Ltd.)

Purpose: This study compared anthropometric and body fat percent (BF%) equations in relation to measures of metabolic health. Methods: BF% calculations (Bergman, Fels, and Woolcott) and anthropometric measurements were used to determine obesity among a sample of patients attending primary care in Alberta, Canada. Anthropometric variables included body mass index (BMI), waist circumference, waist:hip ratio, waist:height ratio, and calculated BF%. Metabolic Z-score was computed as the average of the individual Z-scores of triglycerides, total cholesterol, and fasting glucose and the number of standard deviations from the sample mean. Results : Five hundred and fourteen individuals were included (41.2% male, age: 53 ± 16y, BMI: 27.4 ± 5.7 kg/m 2 ). BMI ≥ 30 kg/m 2 detected the smallest number of participants (n = 137) as having obesity, while Woolcott BF% equation categorized the largest number of participants as having obesity (n = 369). No anthropometric or BF% calculation predicted metabolic Z-score in males (all p  ≥ 0.05). In females, age-adjusted waist:height ratio had the highest prediction power (R 2  = 0.204, p  < 0.001), followed by age-adjusted waist circumference (R 2  = 0.200, p  < 0.001) and age-adjusted BMI (R 2  = 0.178, p  < 0.001). Conclusions : This study did not find evidence that BF% equations more strongly predicted metabolic Z-scores than other anthropometric values. In fact, all anthropometric and BF% variables were weakly related to metabolic health parameters, with apparent sex differences.

Background: Total energy expenditure (TEE) determines energy requirements, but objective data in patients with cancer are limited., Objectives: We aimed to characterize TEE, investigate its predictors, and compare TEE with cancer-specific predicted energy requirements., Methods: This cross-sectional analysis included patients with stages II-IV colorectal cancer from the Protein Recommendation to Increase Muscle (PRIMe) trial. TEE was assessed by 24-h stay in a whole-room indirect calorimeter before dietary intervention and compared with cancer-specific predicted energy requirements (25-30 kcal/kg). Generalized linear models, paired-samples t tests, and Pearson correlation were applied., Results: Thirty-one patients (56 ± 10 y; body mass index [BMI]: 27.9 ± 5.5 kg/m 2 ; 68% male) were included. Absolute TEE was higher in males (mean difference: 391 kcal/d; 95% CI: 167, 616 kcal/d; P < 0.001), patients with colon cancer (mean difference: 279 kcal/d; 95% CI: 73, 485 kcal/d; P = 0.010), and patients with obesity (mean difference: 393 kcal/d; 95% CI: 182, 604 kcal/d; P < 0.001). Appendicular lean soft tissue (β: 46.72; 95% CI: 34.27, 59.17; P < 0.001) and tumor location (colon-β: 139.69; 95% CI: 19.44, 259.95; P = 0.023) independently predicted TEE when adjusted for sex. Error between measured TEE and energy requirements predicted by 25 kcal/kg (mean difference: 241 kcal/d; 95% CI: 76, 405 kcal/d; P = 0.010) or 30 kcal/kg (mean difference: 367 kcal/d; 95% CI: 163, 571 kcal/d; P < 0.001) was higher for patients with obesity, and proportional error was observed (25 kcal/kg: r = -0.587; P < 0.001; and 30 kcal/kg: r = -0.751; P < 0.001). TEE (mean difference: 25 kcal/kg; 95% CI: 24, 27 kcal/kg) was below predicted requirements using 30 kcal/kg (-430 ± 322 kcal/d; P < 0.001)., Conclusions: This is the largest study to assess TEE of patients with cancer using whole-room indirect calorimeter and highlights the need for improved assessment of energy requirements in this population. Energy requirements predicted using 30 kcal/kg overestimated TEE by 1.44 times in a controlled sedentary environment and TEE was outside of the predicted requirement range for most. Special considerations are warranted when determining TEE of patients with colorectal cancer, such as BMI, body composition, and tumor location. This is a baseline cross-sectional analysis from a clinical trial registered at clinicaltrials.gov as NCT02788955 (https://clinicaltrials.gov/ct2/show/NCT02788955)., (Copyright © 2023 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Diet plays a critical role for patients across the cancer continuum. The World Cancer Research Fund International and the American Cancer Society have published evidence supporting the role of nutrition in cancer prevention. We conducted an analysis of the literature on dietary nutrients and cancer to uncover opportunities for future research. The objective of the bibliometric analysis was to describe trends in peer-reviewed publications on dietary components and cancer and to highlight research gaps. PubMed was queried for manuscripts with diet- and cancer-related keywords and Medical Subject Headings (MeSH) terms. Metadata covering 99,784 publications from 6469 journals were analyzed to identify trends since 1970 on diet topics across 19 tumor types. Publications focused largely on breast, colorectal, and liver cancer, with fewer papers linking diet with other cancers such as brain, gallbladder, or ovarian. With respect to "unhealthy" diets, many publications focused on high-fat diets and alcohol consumption. The largest numbers of publications related to "healthy" diets examined the Mediterranean diet and the consumption of fruits and vegetables. These findings highlight the need for additional research focused on under-investigated cancers and dietary components, as well as dietary studies during cancer therapy and post-therapy, which may help to prolong survivorship.

Energy balance accounts for an individual's energy intake, expenditure, and storage. Each aspect of energy balance has implications for the pharmacokinetics of cancer treatments and may impact an individual's drug exposure and subsequently its tolerance and efficacy. However, the integrated effects of diet, physical activity, and body composition on drug absorption, metabolism, distribution, and excretion are not yet fully understood. This review examines the existing literature on energy balance, specifically the role of dietary intake and nutritional status, physical activity and energy expenditure, and body composition on the pharmacokinetics of cancer therapeutics. As energy balance and pharmacokinetic factors can be influenced by age-related states of metabolism and comorbidities, this review also explores the age-related impact of body composition and physiologic changes on pharmacokinetics among pediatric and older adult populations with cancer., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Appetite is a determinant of dietary intake and is impacted by sex hormones, exercise, and body composition among individuals without chronic conditions. Whether appetite is altered by exercise in the context of estrogen suppression and cancer survivorship is unknown. This randomized cross-over study compared appetite and ad libitum energy intake (EI) after acute resistance exercise (REx) versus sedentary (SED) conditions and in relation to body composition and resting metabolic rate (RMR) in breast cancer survivors (BCS). Physically inactive premenopausal females with previous stage I-III estrogen receptor-positive breast cancer completed a single bout of REx or SED 35 minutes after a standardized breakfast meal. Appetite visual analog scales and hormones (total ghrelin and peptide-YY [PYY]) were measured before and 30, 90, 120, 150, and 180 minutes post-meal and expressed as area under the curve (AUC). Participants were offered a buffet-type meal 180 minutes after breakfast to assess ad libitum EI. Body composition (dual X-ray absorptiometry) and RMR (indirect calorimetry) were measured during a separate visit. Sixteen BCS were included (age: 46 ± 2 y, BMI: 24.9 ± 1.0 kg/m 2 ). There were no differences in appetite ratings or EI between conditions. There were no differences in appetite hormone AUC, but REx resulted in lower ghrelin 120 (-85 ± 39 pg/mL, p = 0.031) and 180 (-114 ± 43 pg/mL, p = 0.018) minutes post-breakfast and higher PYY 90 (21 ± 10 pg/mL, p = 0.028) and 120 (14 ± 7 pg/mL, p = 0.041) minutes post-breakfast. Fat-free mass and RMR negatively correlated with hunger and prospective food consumption AUC after SED, but not REx. In sum, a single REx bout temporarily reduces orexigenic and increases anorexic appetite hormones, but not acute subjective appetite sensations or EI., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Introduction/purpose: A reduction in nonexercise physical activity (NEPA) after exercise may reduce the effectiveness of exercise interventions on weight loss in adults with overweight or obesity. Aerobic exercise (AEx) and resistance exercise (REx) may have different effects on NEPA. The purpose of this secondary analysis was to examine the effect of a single bout of AEx or REx on NEPA and sedentary behavior in inactive adults with overweight or obesity., Methods: Adults with overweight or obesity ( n = 24; 50% male; age, 34.5 ± 1.5 yr; body mass index, 28.5 ± 0.9 kg·m -2 ) not meeting current physical activity guidelines completed a single 45-min bout of AEx, REx, or a sedentary control on different days in random order. After each condition, participants' NEPA was recorded for 84 h by accelerometer. Time spent sedentary and in light, moderate, and vigorous physical activity; steps; metabolic equivalent of task (MET)-hours; and sit-to-stand transitions were calculated using activity count data., Results: No differences were observed in the percent of waking time spent sedentary and in light, moderate, and vigorous activity between conditions ( P > 0.05). No differences were observed in steps, MET-hours, or sit-to-stand transitions between conditions ( P > 0.05). NEPA responses were variable among individuals, with approximately half of participants reducing and half increasing NEPA over the 84 h after each exercise condition., Conclusion: NEPA was not reduced after an acute bout of AEx or REx in a sample of inactive adults with overweight or obesity., Competing Interests: The authors report no conflicts of interest. This work was supported by the National Institutes of Health (NIH) grants, such as NIH/National Center for Advancing Translational Sciences under award numbers UL1TR002535, 1UL01TR002538, KL2TR002539, and NIDDK P30DK048520, and NIH/National Institute of Diabetes and Digestive and Kidney Diseases grants T32DK07658 and T32DK007446. This work was also supported by a pilot grant award from the University of Colorado’s Center for Women’s Health Research. This trial was preregistered at ClinicalTrials.gov (NCT03143868). Drs. Melanson and Cornier are supported by resources from Geriatric Research, Education, and the Clinical Center at the Denver VA Medical Center.

Individuals with cancer may be motivated to make lifestyle changes and informed dietary choices, but are exposed to conflicting and erroneous nutrition information, particularly from online and social media sources. Changes to dietary choices that stem from a diagnosis of cancer are not fully understood. Thus, we conducted a narrative review to summarize the literature focused on dietary choices after a cancer diagnosis, and highlighted influential factors. Postdiagnosis dietary changes have been studied primarily in female patients with breast cancer in European countries. Reported changes typically included decreased red and processed meat intake and increased consumption of fruits and vegetables. These changes align with the recommendations for cancer prevention but were implemented after diagnosis, and may not meet nutrition guidelines for patients with cancer. Age and time since diagnosis were among the influential factors that affected these postdiagnosis changes. Data on dietary changes implemented after a diagnosis of varying cancer types in the North American population are lacking, and would provide practitioners with an enhanced understanding of patient information needs and reasons for dietary choices., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Introduction/purpose: Dietary restriction (DIET) and aerobic exercise (AEX) interventions may impact energy balance differently. Our aim was to describe the effects of weight loss interventions via DIET or AEX on measures of energy balance., Methods: Adults with overweight or obesity were randomized to 12 weeks of DIET or AEX with similar calorie deficit goals. A study day was conducted before and after the intervention to assess subjective and hormonal (ghrelin, peptide-YY, glucagon-like peptide-1) appetite responses to a control meal, ad libitum energy intake (EI) at a single meal, and over three days of free-living conditions and eating behavior traits. Resting metabolic rate (RMR) was measured with indirect calorimetry and adjusted for body composition measured by dual X-ray absorptiometry. Non-exercise activity was measured using accelerometers., Results: Forty-four individuals were included (age: 37 ± 9 years, body mass index: 30.6 ± 3.1 kg/m 2 ). Both interventions resulted in weight and fat mass loss. The DIET group lost fat-free mass, although differences between groups were not significant (DIET: -1.2 ± 1.7 kg, p<0.001; AEX: 0.4 ± 1.5 kg, p=0.186; p=0.095 interaction). There were no differences in RMR after body composition adjustment. Both interventions were associated with an increase in dietary restraint (DIET: 4.9 ± 1.2, AEX: 2.8 ± 0.7; p<0.001 in both groups). Hunger decreased with DIET (-1.4 ± 0.5, p=0.003), and disinhibition decreased with AEX (-1.5 ± 0.5, p<0.001), although these changes were not different between groups (i.e., no group × time interaction). No other differences in appetite, EI, or non-exercise physical activity were observed within or between groups., Conclusions: AEX did not result in compensatory alterations in appetite, ad libitum EI, or physical activity, despite assumed increased energy expenditure. Modest evidence also suggested that disinhibition and hunger may be differentially impacted by weight loss modality.

Body composition parameters are not captured by measures of body mass, which may explain inconsistent associations between body weight and prostate cancer (PC) risk. The objective of this systematic review was to characterize the association between fat mass (FM) and fat-free mass (FFM) parameters and PC risk. A search of PubMed, Embase, and Web of Science identified case-control and cohort studies that measured body composition in relation to PC risk. Methodological quality was assessed using the Newcastle-Ottawa Scale (NOS). Thirteen observational studies were included, of which 8 were case-control studies (n = 1572 cases, n = 1937 controls) and 5 were prospective cohort studies (n = 7854 incident cases with PC). The NOS score was 5.9 ± 1.1 for case-control studies and 8.4 ± 1.3 for cohort studies. The most common body composition technique was bioelectrical impedance analysis (n = 9 studies), followed by DXA (n = 2), computed tomography (n = 2), air displacement plethysmography (n = 1), and MRI (n = 1). No case-control studies reported differences in %FM between PC cases and controls and no consistent differences in FM or FFM (in kilograms) were observed. Two out of 5 cohort studies reported that higher %FM was associated with lower PC risk. Conversely, 3 cohort studies reported a greater risk of being diagnosed with advanced/aggressive PC with higher FM (expressed in kilograms, %FM, or fat distribution). Two out of 4 studies (both case-control and cohort) found that higher abdominal adipose tissue was associated with increased PC risk. In conclusion, although results were inconsistent, there is some evidence that FM may be negatively associated with total PC risk but positively associated with the risk of advanced/aggressive PC; modest evidence suggests that abdominal adipose tissue may increase the risk of PC. Future work should elucidate unique patterns of FM distribution and PC risk to triage men at risk for developing PC. This study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database as CRD42019133388., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

The purpose of this study was to evaluate the feasibility and acceptability of randomizing adults with overweight and obesity (BMI 25-40 kg/m 2 ) to morning (06:00-10:00) or evening (15:00-19:00) aerobic exercise. Participants completed four exercise sessions per week in the morning (AM, n = 18) or evening (PM, n = 15). The exercise program was 15 weeks and progressed from 70 to 80% heart rate maximum and 750-2000 kcal/week. Bodyweight, body composition, total daily energy expenditure (TDEE), energy intake (EI), sleep, sedentary behavior (SB), non-exercise physical activity (NEPA), and maximal aerobic capacity were assessed at baseline and week 15. Study retention was 94% and adherence to the supervised exercise program was ≥90% in both groups. Weight change was -0.9 ± 2.8 kg and -1.4 ± 2.3 kg in AM and PM, respectively. AM and PM increased TDEE (AM: 222 ± 399 kcal/day, PM: 90 ± 150 kcal/day). EI increased in AM (99 ± 198 kcal/day) and decreased in PM (-21 ± 156 kcal/day) across the intervention. It is feasible to randomize adults with overweight and obesity to morning or evening aerobic exercise with high levels of adherence. Future trials are needed to understand how the timing of exercise affects energy balance and body weight regulation.

Many breast cancer survivors (BCS) gain fat mass and lose fat-free mass during treatment (chemotherapy, radiation, surgery) and estrogen suppression therapy, which increases the risk of developing comorbidities. Whether these body composition alterations are a result of changes in dietary intake, energy expenditure, or both is unclear. Thus, we reviewed studies that have measured components of energy balance in BCS who have completed treatment. Longitudinal studies suggest that BCS reduce self-reported energy intake and increase fruit and vegetable consumption. Although some evidence suggests that resting metabolic rate is higher in BCS than in age-matched controls, no study has measured total daily energy expenditure (TDEE) in this population. Whether physical activity levels are altered in BCS is unclear, but evidence suggests that light-intensity physical activity is lower in BCS compared to age-matched controls. We also discuss the mechanisms through which estrogen suppression may impact energy balance and develop a theoretical framework of dietary intake and TDEE interactions in BCS. Preclinical and human experimental studies indicate that estrogen suppression likely elicits increased energy intake and decreased TDEE, although this has not been systematically investigated in BCS specifically. Estrogen suppression may modulate energy balance via alterations in appetite, fat-free mass, resting metabolic rate, and physical activity. There are several potential areas for future mechanistic energetic research in BCS (e.g., characterizing predictors of intervention response, appetite, dynamic changes in energy balance, and differences in cancer sub-types) that would ultimately support the development of more targeted and personalized behavioral interventions.

Background: Yoga may reduce body weight in individuals with overweight or obesity, but whether this occurs through decreased energy intake (EI) or increased energy expenditure (EE)/physical activity (PA) is unclear., Methods: A systematic search of PubMed, Web of Science, Embase, and PsychINFO was conducted from inception until April 26, 2021. Eligible studies included randomized controlled trials or single-arm pre-post studies with any type and duration of yoga intervention in adults with overweight or obesity. Studies with measures related to EI , EE, or PA were eligible. The review initially identified 1,373 articles., Results: Of the 10 included studies, one used indirect calorimeter measures of resting EE, while nine used self-reported measures of EI and PA. Of the seven studies measuring parameters related to EI, only one found greater decreases in EI relative to the control group, although three other investigations reported trends toward improved dietary intake. Of the eight studies measuring PA, two reported greater increases in resting EE or PA in the yoga group relative to the control group. Two reported significant within-group increases in PA from pre-post intervention, and four studies reported a trend for increased PA with no p-values reported., Conclusions: Limited evidence suggests yoga may reduce EI and increase PA in adults with overweight or obesity. Additional studies that investigate the effects of yoga interventions on energy balance parameters using objective techniques are warranted., Competing Interests: The authors declare no conflicts of interest., (© 2021 The Authors. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd.)

Sarcopenic obesity is associated with several negative health outcomes. However, the prevalence of this condition - and the relationship to physical performance parameters - varies across definitions. The aim of this cross-sectional investigation was to describe the prevalence of sarcopenic obesity using different published definitions and their relationship with handgrip strength and walking speed in older Canadian adults. Individuals aged 65+ in the Canadian Longitudinal Study on Aging ( n = 11,803; 49.6% male, 50.4% female) were included. Body composition was measured using dual X-ray absorptiometry. Sarcopenic obesity was defined using 29 definitions. Low handgrip strength was identified as < 27 kg in males and < 16 kg in females and poor physical performance was defined as gait speed ≤ 0.8 m/s. The prevalence of sarcopenic obesity ranged from 0.1 to 85.3% in males, and from 0 to 80.4% in females. Sarcopenic obesity was frequently associated with low handgrip strength ( p < 0.05) in both males (14/17 definitions, 82.4%) and females (21/29 definitions, 72.4%). In very few definitions, sarcopenic obesity was associated with slow gait speed (males: 1/17 definitions [6.7%]; females: 2/29 [6.9%]). In conclusion, the prevalence of sarcopenic obesity varied greatly according to definitions and sarcopenic obesity was frequently associated with low handgrip strength., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Purcell, Mackenzie, Barbosa-Silva, Dionne, Ghosh, Siervo, Ye and Prado.)

Background and Aims: The accuracy of some portable indirect calorimeters in estimating resting energy expenditure (REE) - and hence energy recommendations - among pre-bariatric patients is unknown. Our objective was to assess the accuracy of the MedGem® among individuals with class II or III obesity awaiting bariatric surgery., Methods: Male and female adults who were awaiting bariatric surgery were recruited to this cross-sectional study. MedGem-derived oxygen consumption (O 2 ) and REE were compared to O 2 and REE from a metabolic cart. REE was also calculated using several common equations to assess the clinical utility of this portable tool. Body composition was assessed by dual energy X-Ray absorptiometry. Paired t-test and Bland-Altman analysis (expressed as bias [average difference] and limits of agreement [bias ± 2 standard deviations]) evaluated O 2 and REE accuracy., Results: Twenty-six individuals were included (34 ± 9 years old; n = 20, 76.9% female), with the majority (n = 15, 57.7%) having class II obesity. Neither O 2 (MedGem: 283 ± 63 vs. metabolic cart: 293 ± 64 ml/min, p = 0.114) nor REE (MedGem: 1963 ± 437 vs. metabolic cart: 2047 ± 440 kcal/day, p = 0.057) were different between devices. Body composition did not relate to bias. The MedGem had wide limits of agreement for REE (-504 to 336 kcal/day), which was similar to predictive equations., Conclusions: Although REE from the MedGem was not different than REE from the metabolic cart, individual-level accuracy was poor and similar to predictive equations in patients with class II or III obesity. Nonetheless, efficacy of repeated assessments during weight loss warrant future investigation., Competing Interests: Declaration of competing interest There are no conflict of interests with any of the companies/manufacturers of devices/techniques used in this study. This research study included the opinions of the authors; no official endorsement by the Canadian Foundation for Dietetic Research (CFDR) was intended or inferred., (Copyright © 2020 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.)

Background/objectives: Resting energy expenditure (REE) is the largest component of total energy expenditure. As such, inaccurate estimation or measurement of REE may impact the development of individualized energy intake recommendations. The objective of this observational study was to assess the accuracy and reliability of REE measured with a portable indirect calorimeter with ventilated hood - Fitmate GS - compared to REE from whole-body indirect calorimetry (WBC)., Methods: REE was assessed using the Fitmate GS and compared to REE from WBC in male and female adults. Fitmate GS tests were repeated on a separate visit. Body fat percent, fat mass, and fat-free mass were assessed using dual X-ray absorptiometry in a subset of individuals with body mass index (BMI) ≥35 kg/m 2 . Paired t-tests and Bland-Altman analyses were used to describe REE accuracy; intra-class correlation coefficient (ICC) examined test-retest reliability of repeated Fitmate GS assessments., Results: Fitmate GS and WBC REE was assessed in 77 adults (age 32 ± 8 years; 63.6% females; 53.2% with BMI ≥ 30 kg/m 2 ). The Fitmate GS underestimated REE (1680 ± 420 vs. 1916 ± 461 kcal/day, p < 0.001) and wide limits of agreement were observed (95% confidence intervals [CI]: -727, 246 kcal/day). Bias was similar across sex and BMI categories and did not correlate to any body composition parameter. Fitmate GS ICC was 0.80 (95% CI: 0.70-0.87), indicating 'good' reliability with repeated testing after median follow-up of 4 days (interquartile range 2-7 days)., Conclusion: The Fitmate GS did not accurately quantify REE but produced reliable REE measurements., Competing Interests: Conflict of interest disclosure COSMED (Chicago, IL) provided the portable indirect calorimeter (Fitmate GS) used in this study. COSMED did not provide financial support nor played a role in data analysis or drafting of the manuscript. This research study included the opinions of the authors; no official endorsement by the Canadian Foundation for Dietetic Research (CFDR) was intended or inferred., (Copyright © 2020 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.)

Many patients with cancer experience poor nutritional status, which detrimentally impacts clinical outcomes. Poor nutritional status in cancer is primarily manifested by severe muscle mass (MM) depletion, which may occur at any stage (from curative to palliative) and often co-exists with obesity. The objective of this article was to discuss gaps and opportunities related to the role of nutrition in preventing and reversing low MM in cancer. It also provides a narrative review of relevant nutritional interventions for patients capable of oral intake. The impact of nutrition interventions to prevent/treat low MM in cancer is not well understood, potentially due to the limited number of studies and of clinically viable, accurate body composition assessment tools. Additionally, the type of study designs, inclusion criteria, length of intervention, and choice of nutritional strategies have not been optimal, likely underestimating the anabolic potential of nutrition interventions. Nutrition studies are also often of short duration, and interventions that adapt to the metabolic and behavioural changes during the clinical journey are needed. We discuss energy requirements (25-30 kcal/kg/day) and interventions of protein (1.0-1.5 g/kg/day), branched-chain amino acids (leucine: 2-4 g/day), β-hydroxy β-methylbutyrate (3 g/day), glutamine (0.3 g/kg/day), carnitine (4-6 g/day), creatine (5 g/day), fish oil/eicosapentanoic acid (2.0-2.2 g/day EPA and 1.5 g/day DHA), vitamin/minerals (e.g. vitamin D: 600-800 international units per day), and multimodal approaches (nutrition, exercise, and pharmaceutical) to countermeasure low MM in cancer. Although the evidence is variable by modality type, interventions were generally not specifically studied in the context of cancer. Understanding patients' nutritional requirements could lead to targeted prescriptions to prevent or attenuate low MM in cancer, with the overall aim of minimizing muscle loss during anti-cancer therapy and maximizing muscle anabolism during recovery. It is anticipated that this will, in turn, improve overall health and prognostication including tolerance to treatment and survival. However, oncology-specific interventions with more robust study designs are needed to facilitate these goals., (© 2020 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of the Society on Sarcopenia, Cachexia and Wasting Disorders.)

Background & Aims: Resting energy expenditure (REE) is variable in cancer and might be influenced by changes in tumor burden, systemic inflammation, and body composition. The objective of this study was to assess REE change and the predictors of such in patients with stage III or IV colorectal cancer., Methods: REE was measured via indirect calorimetry and fat mass and fat-free mass (FFM) were assessed using dual X-ray absorptiometry as part of a unique analysis of two studies. C-reactive protein (CRP) was measured as an inflammatory marker. Linear regression was used to assess the determinants of REE at baseline and REE change, with days between baseline and follow-up measures included as a covariate., Results: One-hundred and nine patients were included at baseline (59.6% male; 67 ± 12 years; body mass index 24.1 ± 4.3 kg/m 2 ); 49 had follow-up data (61.2% male; 65 ± 12 years; body mass index 25.4 ± 4.3 kg/m 2 ), with median follow-up of 119 days (interquartile range: 113-127 days). At baseline, age, FFM, and CRP explained 68.9% of the variability in REE. A wide variability in REE change over time was observed, ranging from -156 to 370 kcal/day, or -13.0 to 15.7%/100 days. CRP change (1.7 ± 0.4 mg/L, p < 0.001) and stage (81.3 ± 38.7, p = 0.042) predicted REE change in multivariate analysis, controlling for age, FFM change, and days between visits (R 2 : 0.417 ± 88.2, p < 0.001)., Conclusions: Age, FFM, and CRP predicted REE at a single time point. REE change was highly variable and explained by inflammation and stage. Future research should investigate the validity and feasibility of incorporating these measures into energy needs recommendations., (Copyright © 2019 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

Background: Understanding resting energy expenditure (REE) is important for determining energy requirements; REE might be altered in individuals with cancer. The objective of this study was to characterize determinants of REE in patients with stages II-IV colorectal cancer (CRC). Methods: REE was measured via indirect calorimetry in patients with newly diagnosed CRC. Computerized tomography images from medical records ascertained skeletal muscle and total adipose tissue cross-sectional areas, which were then transformed to lean soft tissue (LST) and fat mass (FM) values (in kg). Linear regression assessed determinants of REE. Results: 86 patients were included ( n  = 55, 64.0% male; 60 ± 12 years old; median body mass index: 27.6, interquartile range: 24.3-31.2 kg/m 2 ), with most ( n  = 40) having stage III disease. Age, sex, and weight were significant predictors of REE [ R 2 = 0.829, standard error of the estimate (SEE): 128 kcal/day, P  < 0.001]. Replacing weight with LST and FM yielded a similar model, with age, sex, LST, and FM predictive of REE ( R 2 = 0.820, SEE: 129 kcal/day, p  < 0.001). Conclusion: Age, sex, weight, LST, and FM were the main contributors to REE. Further investigation of REE changes over time and its relationship to total energy expenditure, dietary intake, and clinical outcomes should be explored.

Objectives: Sarcopenia is a debilitating condition affecting millions of individuals worldwide and is defined with different criteria. The objective of this study was to determine the prevalence of sarcopenia in older Canadians using three internationally accepted criteria., Design: Observational cohort study., Settings and Participants: Data from 12,592 subjects [6,314 males (50.1%), 6,278 females (49.9%)] ≥65 years old in the Canadian Longitudinal Study on Aging were included., Measurements: Appendicular lean mass (ALM; kg) and appendicular lean mass index (ALM kg/height in m2) were collected from dual X-ray absorptiometry measurements. Physical performance was assessed using the 4-m gait speed test, and muscle strength was measured by hand dynamometry. Sarcopenia was defined according to criteria put forth by the International Working Group on Sarcopenia (IWGS), Foundation for the National Institutes of Health (FNIH) Sarcopenia Project, and revised European Working Group on Sarcopenia in Older People (EWGSOP). Positive and negative percent agreements and Cohen's kappa (κ) described the agreement among sarcopenia definitions., Results: Among the evaluated criteria, gait speed ≤ 1.0 m/s (IWGS definition of slowness) was the most frequently identified deficit (56.8% males, 57.2% females). The prevalence of sarcopenia ranged from 1.4 to 5.2% in males and 1.6 to 7.2 % in females among the different definitions. Positive percent agreement values among criteria were generally low (range: 1.5 - 55.3%) and corresponded to κ indicating none to minimal agreement (0.01 - 0.23). Negative percent agreement values were ≥ 95%., Conclusion: Sarcopenia prevalence was relatively low in older Canadian adults and current definitions had poor agreement in diagnosing individuals as sarcopenic., Competing Interests: The authors have no conflicts of interests to declare.

Background: Our purpose was to assess the accuracy of resting energy expenditure (REE) equations in patients with newly diagnosed stage I-IV non-small cell lung, rectal, colon, renal, or pancreatic cancer., Methods: In this cross-sectional study, REE was measured using indirect calorimetry and compared with 23 equations. Agreement between measured and predicted REE was assessed via paired t-tests, Bland-Altman analysis, and percent of estimations ≤ 10% of measured values. Accuracy was measured among subgroups of body mass index (BMI), stage (I-III vs IV), and cancer type (lung, rectal, and colon) categories. Fat mass (FM) and fat-free mass (FFM) were assessed using dual x-ray absorptiometry., Results: Among 125 patients, most had lung, colon, or rectal cancer (92%, BMI: 27.5 ± 5.6 kg/m 2 , age: 61 ± 11 years, REE: 1629 ± 321 kcal/d). Thirteen (56.5%) equations yielded REE values different than measured (P < 0.05). Limits of agreement were wide for all equations, with Mifflin-St. Jeor equation having the smallest limits of agreement, -21.7% to 11.3% (-394 to 203 kcal/d). Equations with FFM were not more accurate except for one equation (Huang with body composition; bias, limits of agreement: -0.3 ± 11.3% vs without body composition: 2.3 ± 10.1%, P < 0.001). Bias in body composition equations was consistently positively correlated with age and frequently negatively correlated with FM. Bias and limits of agreement were similar among subgroups of patients., Conclusion: REE cannot be accurately predicted on an individual level, and bias relates to age and FM., (© 2019 American Society for Parenteral and Enteral Nutrition.)

Background: Total energy expenditure (TEE) data in patients with early-stage cancer are scarce, precluding an understanding of energy requirements., Objective: The objective was to cross-sectionally characterize TEE in patients with colorectal cancer (CRC) and to compare measured TEE with energy recommendations. It was hypothesized that TEE would differ according to body mass, body composition, and physical activity level (PAL) and current energy recommendations would have poor individual-level accuracy., Methods: Patients with newly diagnosed CRC had resting energy expenditure (REE) measured by indirect calorimetry and TEE by doubly labeled water. Hypermetabolism was defined as REE > 110% of that predicted from the Mifflin St.-Jeor equation. Body composition was assessed via DXA. Physical activity was determined as the ratio of TEE to REE (TEE:REE) (PAL) and residual activity energy expenditure (RAEE). TEE was compared with energy recommendations of 25-30 kcal/d and Dietary Reference Intakes (DRIs) using Bland-Altman analyses. Patients were stratified according to median BMI, PAL, and sex-specific ratio of fat mass (FM) to fat-free mass (FFM)., Results: Twenty-one patients (M:F 14:7; mean ± SD BMI: 28.3 ± 4.9 kg/m2, age: 57 ± 12 y) were included. Most (n = 20) had stage II-III disease; 1 had stage IV. Approximately half (n = 11) were hypermetabolic; TEE was not different in those with hypermetabolism and REE as a percentage of predicted was not correlated with TEE. Mean ± SD TEE was 2473 ± 499 kcal/d (range: 1562-3622 kcal/d), or 29.7 ± 6.3 kcal/kg body weight (range: 20.4-48.5 kcal/kg body weight). Mean ± SD PAL was 1.43 ± 0.27. The energy recommendation of 25 kcal/kg underestimated TEE (-12.6% ± 16.5%, P = 0.002); all energy recommendations had wide limits of agreement (the smallest was DRI with measured PAL: -21.2% to 29.3%). Patients with higher BMI and FM:FFM had higher bias using kilocalories per kilogram recommendations; bias from several recommendations was frequently lower (i.e. underestimation) in patients with higher PAL and RAEE., Conclusions: TEE variability was not reflected in energy recommendations and error was related to body weight, body composition, and physical activity. This trial was registered at clinicaltrials.gov as NCT03131921., (Copyright © American Society for Nutrition 2019.)

Background: Accurate assessment of energy expenditure may support weight-management recommendations. Measuring energy expenditure for each postpartum woman is unfeasible; therefore, accurate predictive equations are needed., Objectives: This study compared measured with predicted resting energy expenditure (REE) and total energy expenditure (TEE) in postpartum women., Methods: This was a longitudinal observational study. REE was measured at 3 mo postpartum (n = 52) and 9 mo postpartum (n = 49), whereas TEE was measured once at 9 mo postpartum (n = 43) by whole body calorimetry (WBC). Measured REE (REEWBC) was compared with 17 predictive equations; measured TEE plus breast milk energy output (ERWBC) was compared with the estimated energy requirements/Dietary Reference Intakes equation (EERDRI). Fat and fat-free mass were measured by dual-energy X-ray absorptiometry. Group-level agreement was assessed by the Pearson correlation, paired t test, and Bland-Altman (bias) analyses. Individual-level accuracy was assessed with the use of Bland-Altman limits of agreement, and by the percentage of women with predicted energy expenditure within 10% of measured values ("accuracy")., Results: The cohort was primarily Caucasian (90%). At a group level, the best equation predicting REEWBC was the DRI at 3 mo postpartum (-7 kcal, -0.1%; absolute and percentage bias, respectively), and the Harris-Benedict at 9 mo postpartum (-17 kcal, -0.5%). At an individual level, the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU) height and weight equation was the most accurate at 3 mo postpartum (100% accuracy) and 9 mo postpartum (98% accuracy), with the smallest limits of agreement. Equations including body composition variables were not more accurate. Compared with ERWBC, EERDRI bias was -36 kcal, with inaccurate predictions in 33% of women., Conclusions: Many REE predictive equations were accurate for group assessment, with the FAO/WHO/UNU height and weight equation having the highest accuracy for individuals. EERDRI performed well at a group level, but inaccurately for 33% of women. A greater understanding of the physiology driving energy expenditure in the postpartum period is needed to better predict TEE and ultimately guide effective weight-management recommendations., (© 2019 American Society for Nutrition.)

Background: Determining optimal caloric intake for an individual with cancer is complicated by metabolic changes that occur, namely, alterations in resting energy expenditure (REE). There is currently no validated clinically available equation or tool to measure energy expenditure in these patients., Methods: Patients with newly diagnosed solid tumors underwent REE assessments using the FitMate GS portable indirect calorimeter and reference VMax metabolic cart; both used canopy hoods. REE was also estimated from the Harris-Benedict, Mifflin St. Jeor, and Henry equations for comparison. Data were analyzed using paired samples t-test and the Bland-Altman approach to assess group-level and individual-level agreement compared with the metabolic cart., Results: A total 26 patients (19 males; body mass index: 27.8 ± 5.5 kg/m 2 ; age: 62 ± 10 years) participated in the study. Biases for the FitMate GS and both equations were low (ranging from -44 to -92 kcal or -2.3% to -5.1%), indicating good group-level accuracy. The FitMate GS had low bias, but the widest limits of agreement (-28.0% to 21.2%) compared with the 3 equations (Harris-Benedict: -15.8% to 11.2%; Mifflin St. Jeor: -17.1% to 6.9%; Henry: -15.4% to 11.5%). These differences were not due to volume of oxygen, BMI category, or sex., Conclusion: FitMate GS performed well on a group level, but its accuracy was poor on an individual level. Further research should develop better equations and validate tools to measure energy expenditure for accurate dietary recommendations for patients at nutrition risk., (© 2018 American Society for Parenteral and Enteral Nutrition.)

Abnormalities in body composition can occur at any body weight. Low muscle mass is a predictor of poor morbidity and mortality and occurs in several populations. This narrative review provides an overview of the importance of low muscle mass on health outcomes for patients in inpatient, outpatient and long-term care clinical settings. A one-year glimpse at publications that showcases the rapidly growing research of body composition in clinical settings is included. Low muscle mass is associated with outcomes such as higher surgical and post-operative complications, longer length of hospital stay, lower physical function, poorer quality of life and shorter survival. As such, the potential clinical benefits of preventing and reversing this condition are likely to impact patient outcomes and resource utilization/health care costs. Clinically viable tools to measure body composition are needed for routine screening and intervention. Future research studies should elucidate the effectiveness of multimodal interventions to counteract low muscle mass for optimal patient outcomes across the healthcare continuum. Key messages Low muscle mass is associated with several negative outcomes across the healthcare continuum. Techniques to identify and counteract low muscle mass in clinical settings are needed.

Background & Aim: Low fat-free mass (FFM) or high fat mass (FM) are abnormal body composition phenotypes associated with morbidity. These conditions in combination lead to worse health outcomes, and can be identified by a high FM/FFM ratio. Here, we developed sex, age, and body mass index (BMI) stratified, population-based FM/FFM reference values using bioelectrical impedance analysis (BIA) measurements., Methods: White, non-Hispanic individuals aged 18-90 years old with data for weight, stature and BIA resistance measures from the third National Health and Nutrition Examination Survey (NHANES) III were included. Previously validated and sex-specific BIA prediction equations were used to calculate FM and FFM. FM/FFM values were generated at 5th, 50th and 95th percentiles for each sex, age (18-39.9, 40-59.9, 60-69.9 and 70-90 years), and BMI category (underweight, normal weight, overweight, class I/II and class III obesity)., Results: A total of 6372 individuals who had estimated FM and FFM values were identified (3366 females, 3006 males). Median values of FM/FFM were 0.24 and 0.40 for young (≤39.9 years) males and females with normal BMI, and 0.34 for males and 0.59 for females who were overweight. For elderly individuals aged >70 years, median FM/FFM for males and females were respectively 0.28 and 0.45 for those with normal BMI, and 0.37 and 0.61 for those in the overweight category., Conclusions: These FM/FFM reference values provide information on body composition characteristics that account for age, sex and BMI, which can be useful to identify individuals at risk for body composition abnormalities., (Copyright © 2017 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

Aim: To investigate the prevalence of sarcopenic obesity (SO) and its association with health outcomes in patients seeking weight loss treatment from a bariatric center., Methods: In this retrospective study, patients [≥18 years old, body mass index (BMI) ≥30 kg/m 2 ] from the Tallahassee Memorial Bariatric Center and with baseline body composition assessment by bioelectrical impedance analysis were included. Fat mass index (FMI = fat mass/height 2 ) and fat-free mass index (FFMI = fat free mass/height 2 ) were calculated. SO was defined by a FMI/FFMI ratio greater than the 95 percentile of sex, BMI and ethnicity specific population-representative references. Medical records were reviewed for biochemical and comorbidity measures., Results: One hundred and forty-four patients (∼69% females, mean age 55.6 years, mean BMI 46.6 kg/m 2 ) were included. Patients' FMI/FFMI ratios ranged from 0.35 to 1.60 kg/m 2 across body weight spectrum, with 51% having SO. Blood pressure, fasting glucose, triglycerides, HDL or LDL were not different between patients with and without SO. However, the prevalence of high cholesterol, asthma, alcoholism and hernia were higher in patients with SO. SO was the strongest univariate predictor of high cholesterol (OR = 2.08, 95% CI 1.07-4.04) and asthma (OR = 2.77, 95% CI = 1.12-6.83)., Conclusion: SO was prevalent and associated with adverse health outcomes, beyond that captured by anthropometric measures in the present study., (Copyright © 2017 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.)

Great discrepancies exist in the reported prevalence of altered energy metabolism (hypo- or hypermetabolism) in cancer patients, which is likely due to the vast array of phenomena that can affect energy expenditure in these patients. The purpose of this review was to critically evaluate key determinants of energy expenditure in cancer and the relevance for clinical practice. Resting energy expenditure (REE) is the largest and most commonly measured component of total energy expenditure. In addition to the energetic demand of the tumor itself, REE may be increased due to changes in inflammation, body composition and brown adipose tissue activation. Energy expenditure from physical activity is often lower in cancer compared with healthy populations, and there is evidence to suggest that the thermic effect of food might also be blunted and affected by cancer therapy. Although accurate assessment of energy metabolism is a cornerstone of adequate nutritional therapy, prediction methods often do not capture the true energy expenditure of most cancer patients. In fact, limits of agreement of prediction equations may range from 40% below to 30% above measured REE. Such variability highlights the need for a more comprehensive understanding of energy expenditure in cancer and the value of accurately assessing the energy needs of these patients.

Measures of body weight and anthropometrics such as body mass index (BMI) are commonly used to assess nutritional status in clinical conditions including cancer. Extensive research has evaluated associations between body weight and prognosis in ovarian cancer patients, yet little is known about the potential impact of body composition (fat mass (FM) and fat-free mass (FFM)) in these patients. Thus, the purpose of this publication was to review the literature (using PubMed and EMBASE) evaluating the impact of body weight and particularly body composition on surgical complications, morbidity, chemotherapy dosing and toxicity (as predictors of prognosis), and survival in ovarian cancer patients. Body weight is rarely associated with intra-operative complications, but obesity predicts higher rates of venous thromboembolism and wound complications post-operatively in ovarian cancer patients. Low levels of FM and FFM are superior predictors of length of hospital stay compared to measures of body weight alone, but the role of body composition on other surgical morbidities is unknown. Obesity complicates chemotherapy dosing due to altered pharmacokinetics, imprecise dosing strategies, and wide variability in FM and FFM. Measurement of body composition has the potential to reduce toxicity if the results are incorporated into chemotherapy dosing calculations. Some findings suggest that excess body weight adversely affects survival, while others find no such association. Limited studies indicate that FM is a better predictor of survival than body weight in ovarian cancer patients, but the direction of this relationship has not been determined. In conclusion, body composition as an indicator of nutritional status is a better prognostic tool than body weight or BMI alone in ovarian cancer patients.