Summary
Physical activity and body composition show a typical pattern over the lifecycle. Fat‐free mass and physical performance generally peak in early adulthood. Here, evidence for a relation ...between physical activity changes over the life span and the development of sarcopenic obesity is presented. Activity‐induced energy expenditure increases with body size and physical activity during growth. The physical activity level, calculated by expressing total energy expenditure as a multiple of resting energy expenditure, gradually increases from early age to adulthood to decrease again in old age. Habitual physical activity has a significant effect on growth of fat‐free mass during adolescence and thus on peak fat‐free mass and physical performance in early adulthood. Older subjects have a lower fat‐free mass and lower physical activity levels but there is no association, suggesting physical activity does not protect against loss of lean body mass at higher age. Prevention of sarcopenic obesity starts with a physically active lifestyle to develop a healthy peak fat‐free mass and subsequent prevention of excess fat gain. The change from a physically active to a more sedentary routine in later life requires restriction of energy intake to maintain energy balance.
Because of the key role played by the body's lean tissue reserves (of which skeletal muscle is a major component) in the response to injury and illness, its maintenance is of central importance to ...nutrition status. With the recent development of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition diagnostic framework for malnutrition, the loss of muscle mass has been recognized as one of the defining criteria. Objective methods to evaluate muscle loss in individuals with acute and chronic illness are needed. Bioimpedance and ultrasound techniques are currently the best options for the clinical setting; however, additional research is needed to investigate how best to optimize measurements and minimize error and to establish if these techniques (and which specific approaches) can uniquely contribute to the assessment of malnutrition, beyond more subjective evaluation methods. In this tutorial, key concepts and statistical methods used in the validation of bedside methods to assess lean tissue compartments are discussed. Body composition assessment methods that are most widely available for practice and research in the clinical setting are presented, and clinical cases are used to illustrate how the clinician might use bioimpedance and/or ultrasound as a tool to assess nutrition status at the bedside. Future research needs regarding malnutrition assessment are identified.
Summary
Postbariatric loss of muscle tissue could negatively affect long‐term health due to its role in various bodily processes, such as metabolism and functional capacity. This meta‐analysis aimed ...to unravel time‐dependent changes in the magnitude and progress of lean body mass (LBM), fat‐free mass (FFM), and skeletal muscle mass (SMM) loss following bariatric surgery. A systematic literature search was conducted in Pubmed, Embase, and Web of Science. Fifty‐nine studies assessed LBM (n = 37), FFM (n = 20), or SMM (n = 3) preoperatively and ≥1 time points postsurgery. Random‐effects meta‐analyses were performed to determine pooled loss per outcome parameter and follow‐up time point. At 12‐month postsurgery, pooled LBM loss was −8.13 kg 95%CI −9.01; −7.26. FFM loss and SMM loss were −8.23 kg 95%CI −10.74; −5.73 and −3.18 kg 95%CI −5.64; −0.71, respectively. About 55% of 12‐month LBM loss occurred within 3‐month postsurgery, followed by a more gradual decrease up to 12 months. Similar patterns were seen for FFM and SMM. In conclusion, >8 kg of LBM and FFM loss was observed within 1‐year postsurgery. LBM, FFM, and SMM were predominantly lost within 3‐month postsurgery, highlighting that interventions to mitigate such losses should be implemented perioperatively.
Background
Despite malnutrition being associated with increased mortality and morbidity, there continues to be great difficulty in defining criteria and implementing widespread screening. Tools used ...to diagnose decreased fat‐free mass (FFM sarcopenia) should be easy to use, relatively inexpensive, and safe. Bioelectrical impedance analysis (BIA) has the potential to meet these criteria, but reliability across body mass index (BMI) classes is a concern.
Methods
A total of 176 healthy ambulatory participants (aged 18–65 years) were recruited equally (n = 44) in 4 BMI categories: (1) 18.5–24.9, (2) 25.0–29.9, (3) 30–34.9, and (4) ≥35.0. Participants were fasting overnight and had S‐MFBIA (InBody 770) measurements the next morning, with DXA being performed subsequently within 30 minutes.
Results
The measurement (mean ± SD) for FFM with DXA was 52.8 ± 11.0, and BIA was 53.6 ± 11.0. Delta (S‐MFBIA vs DXA) was 0.8 ± 2.2 (5% limits of agreement −3.5 to +5.2), and concordance correlation coefficient (CCC) was 0.98 (95% CI, 0.97–0.98). The measurements (mean ± SD) for PBF with DXA was 37.5 ± 10.6% and S‐MFBIA was 36.6 ± 11.3%. Delta (S‐MFBIA vs DXA) was −0.9 ± 2.6 (5% limits of agreement 6.0 to +4.2), and CCC was 0.97 (95% CI, 0.96–0.98). The CCC according to the 4 BMI groups for FFM and PBF was between 0.96–0.98 and 0.90–0.94, respectively.
Conclusions
FFM and PBF measured by S‐MFBIA had good agreement with DXA across all BMI categories measured in the current study of ambulatory participants.
The Global Leadership Initiative on Malnutrition (GLIM) has suggested a process for the diagnosis of malnutrition. The process consists of applying an existing screening tool for malnutrition ...screening, followed by malnutrition diagnostics, and finally categorization of malnutrition severity (moderate or severe) according to specific GLIM criteria. However, it is not known how well the GLIM process agrees with other diagnostic tools used in the current clinical practice. The aim of this study was to validate the GLIM process against the Patient Generated-Subjective Global Assessment (PG-SGA) when different screening tools were applied in the screening step of the GLIM process.
Colorectal cancer (CRC) patients from the ongoing CRC-NORDIET study were included. For the GLIM process, the patients were first screened for malnutrition using either 1) Nutritional risk screening, first 4 questions (NRS-2002-4Q), 2) Malnutrition Screening Tool (MST), 3) Malnutrition Universal Screening Tool (MUST) or 4) the PG-SGA short form (PG-SGA-SF). The GLIM malnutrition diagnosis was then based on combining the result from each of the screening methods with the etiological and phenotypic GLIM-criteria including weight loss, BMI and fat free mass. In parallel, the patients were diagnosed using the PG-SGA methodology categorizing the patients into either A: well nourished, B: moderately malnourished or C: severely malnourished. The four different GLIM based diagnoses were then validated against the diagnosis obtained by the PG-SGA tool. Sensitivity, specificity and positive predictive value (PPV) were calculated to evaluate validity.
In total, 426 patients were included (mean age: 66, ±8 years) at a mean time of 166 (±56) days after surgery. The GLIM diagnosis based on the four different screening tools identified 10–24% of the patients to be malnourished, of which 3–8% were severely malnourished. The PG-SGA method categorized 15% as moderately malnourished (PG-SGA: category B) and no patients as severely malnourished (PG-SGA: category C). The agreement between the PG-SGA and GLIM process was in general low, but differed according to the tools: PG-SGA SF (sensitivity 47%, PPV 71%), MST (sensitivity 56%, PPV 47%), NRS-2002-4Q (sensitivity 63%, PPV 53%) and MUST (sensitivity 53%, PPV 34%).
In this cross-sectional study of patients with CRC, the concordance between the GLIM-criteria and PG-SGA depended on the screening tool used in the GLIM process. Malnutrition frequency based on the GLIM process schould be reported with and without the use of a screening tool.
This study investigated the effects of a relatively high‐ versus moderate‐volume resistance training program on changes in lean mass during caloric restriction. Thirty‐eight resistance‐trained males ...were randomized to perform either a high‐volume (HVG; 5 sets/exercise) or a moderate‐volume (MVG; 3 sets/exercise) resistance training program. Both groups were supervised during lower body training. Participants consumed 30 kcal/kg for 6 weeks after 1 week of weight maintenance (45 kcal/kg), with protein intake fixed at 2.8 g/kg fat‐free mass. Muscle thickness of the m. rectus femoris, body composition, contractile properties, stiffness, mood, and sleep status were assessed at pre‐, mid‐, and post‐study. No significant group × time interaction was observed for muscle thickness of the m. rectus femoris at 50% (∆ post‐pre 0.36 ± 0.93 mm vs. ∆ −0.01 ± 1.59 mm; p = 0.226) and 75% length (∆ −0.32 ± 1.12 mm vs. ∆ 0.08 ± 1.14 mm; p = 0.151), contractility, sleep, and mood in the HVG and MVG, respectively. Body mass (HVG: ∆ −1.69 ± 1.12 kg vs. MVG: ∆ −1.76 ± 1.76 kg) and lean mass (∆ −0.51 ± 2.30 kg vs. ∆ −0.92 ± 1.59 kg) decreased significantly in both groups (p = 0.022), with no between‐group difference detected (p = 0.966). High‐volume resistance training appears to have neither an advantage nor disadvantage over moderate‐volume resistance training in terms of maintaining lean mass or muscle thickness. Given that both groups increased volume load and maintained muscle contractility, sleep quality, and mood, either moderate or higher training volumes conceivably can be employed by resistance‐trained individuals to preserve muscle during periods of moderate caloric restriction.
Background
Understanding how blood alcohol concentrations (BAC) achieved after drinking are determined is critical to predicting alcohol exposure to the brain and other organs and alcohol's effects. ...However, predicting end‐organ exposures is challenging, as there is wide variation in BAC achieved after drinking a specified volume of alcohol. This variation is partly due to differences in body composition and alcohol elimination rates (AER), but there are limited data on how obesity affects AER. Here, we assess associations between obesity, fat‐free mass (FFM), and AER in women and examine whether bariatric surgeries, which are linked to an increased risk of alcohol misuse, affect these associations.
Methods
We analyzed data from three studies that used similar intravenous alcohol clamping procedures to estimate AER in 143 women (21 to 64 years old) with a wide range of body mass index (BMI; 18.5 to 48.4 kg/m2). Body composition was measured in a subgroup using dual‐energy X‐ray absorptiometry (n = 42) or Bioimpedance (n = 60), and 19 of the women underwent bariatric surgery 2.1 ± 0.3 years before participation. We analyzed data using multiple linear regression analyses.
Results
Obesity and older age were associated with a faster AER (BMI: rs = 0.70 and age: rs = 0.61, both p < 0.001). Compared to women with normal weight, AER was 52% faster (95% Confidence Interval: 42% to 61%) in women with obesity. However, BMI lost predictive value when adding fat‐free mass (FFM) to the regression model. Age, FFM, and its interaction explained 72% of individual variance in AER (F (4, 97) = 64.3, p < 0.001). AER was faster in women with higher FFM, particularly women in the top tertile of age. After controlling for FFM and age, bariatric surgery was not associated with differences in AER (p = 0.74).
Conclusions
Obesity is associated with a faster AER, but this association is mediated by an obesity‐related increase in FFM, particularly in older women. Previous findings of a reduced alcohol clearance following bariatric surgery compared with prior to surgery are likely explained by a reduction in FFM post‐surgery.
Using the intravenous alcohol clamp method to estimate alcohol elimination rates (AER) in 143 women, we show here that obesity was associated with 52% faster AER. However, this association depended on an obesity‐related increase in fat‐free mass (FFM), a good predictor of lean liver volume. Age and FFM explained 72% of individual variations in AER. Bariatric surgeries, which remarkably increase the bioavailability of ingested alcohol and the risk for alcohol misuse, were not associated with differences in AER.
Summary
Reliable and valid body composition assessment is important in both clinical and research settings. A multitude of methods and techniques for body composition measurement exist, all with ...inherent problems, whether in measurement methodology or in the assumptions upon which they are based. This review is focused on currently applied methods for in vivo measurement of body composition, including densitometry, bioimpedance analysis, dual‐energy X‐ray absorptiometry, computed tomography (CT), magnetic resonance techniques and anthropometry. Multicompartment models including quantification of trace elements by in vivo neutron activation analysis, which are regarded as gold standard methods, are also summarized. The choice of a specific method or combination of methods for a particular study depends on various considerations including accuracy, precision, subject acceptability, convenience, cost and radiation exposure. The relative advantages and disadvantages of each method are discussed with these considerations in mind.
Anorexia nervosa (AN) occurs nine times more often in females than in males. Although environmental factors likely play a role, the reasons for this imbalanced sex ratio remain unresolved. AN ...displays high genetic correlations with anthropometric and metabolic traits. Given sex differences in body composition, we investigated the possible metabolic underpinnings of female propensity for AN. We conducted sex‐specific GWAS in a healthy and medication‐free subsample of the UK Biobank (n = 155,961), identifying 77 genome‐wide significant loci associated with body fat percentage (BF%) and 174 with fat‐free mass (FFM). Partitioned heritability analysis showed an enrichment for central nervous tissue‐associated genes for BF%, which was more prominent in females than males. Genetic correlations of BF% and FFM with the largest GWAS of AN by the Psychiatric Genomics Consortium were estimated to explore shared genomics. The genetic correlations of BF%male and BF%female with AN differed significantly from each other (p < .0001, δ = −0.17), suggesting that the female preponderance in AN may, in part, be explained by sex‐specific anthropometric and metabolic genetic factors increasing liability to AN.
Summary
It is now recognized that the amount and type of dietary fat consumed play an important role in metabolic health. In humans, high intake of polyunsaturated fatty acids (PUFAs) has been ...associated with reductions in cardiovascular disease risk, improvements in glucose homeostasis, and changes in body composition that involve reductions in central adiposity and, more recently, increases in lean body mass. There is also emerging evidence, which suggests that high intakes of the plant‐based essential fatty acids (ePUFAs)—n‐6 linoleic acid (LA) and n‐3 α‐linolenic acid (ALA)—have a greater impact on body composition (fat mass and lean mass) and on glucose homeostasis than the marine‐derived long‐chain n‐3 PUFA—eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). In addition, high intake of both ePUFAs (LA and ALA) may also have anti‐inflammatory effects in humans. The purpose of this review is to highlight the emerging evidence, from both epidemiological prospective studies and clinical intervention trials, of a role for PUFA, in particular ePUFA, in the long‐term regulation of body weight and body composition, and their impact on cardiometabolic health. It also discusses current notions about the mechanisms by which PUFAs modulate fat mass and lean mass through altered control of energy intake, thermogenesis, or lean–fat partitioning.
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