Dialysis Duration, Time Interaction, and Visceral Fat Accumulation: A 6-Year Posttransplantation Study.

BACKGROUND: Kidney transplantation (KT) leads to body composition change, particularly increasing the fat mass. However, limited researches have focused on the long-term follow-up of these changes and factors influencing body composition after KT. METHODS: This study evaluated body composition in 31 adult KT recipients, measuring body mass index (BMI), the psoas muscle mass index (PMI) representing muscle mass, visceral and subcutaneous adipose tissue (VAT and SAT) representing fat mass, and skeletal muscle radiodensity (SMR) representing muscle quality before KT and at 2, 4, and 6 years posttransplantation using computed tomography. Linear mixed models (LMM) analyzed temporal changes and contributing factors, while growth curve models assessed influence of these factors on body composition changes posttransplantation. RESULTS: Following KT, BMI, and PMI remained stable, while SAT increased significantly, revealing a 1.30-fold increase from baseline 2 years after transplantation. Similarly, a substantial increase in VAT was observed, with a 1.47-fold increase from baseline 2 years after transplantation with a further 1.75-fold increase 6 years after transplantation. In contrast, SMR decreased with a 0.86-fold decrease from baseline after 2 years. VAT increase was significantly influenced by the interaction between posttransplantation and dialysis duration. Growth curve models confirmed this interaction effect persistently influenced VAT increase posttransplantation. CONCLUSIONS: The study revealed that KT promoted significant alterations in body composition characterized by increase in the VAT and SAT and a decline in SMR. Notably, dialysis duration and its interaction with posttransplantation duration emerged as significant factors influencing VAT increase.

Comparison of Bioelectrical Impedance Analysis and Computed Tomography on Body Composition Changes Including Visceral Fat After Bariatric Surgery in Asian Patients with Obesity.

PURPOSE: The accuracy of bioelectrical impedance analysis (BIA) in patients with obesity has been controversial. This study aimed to validate the use of BIA in detecting body composition changes, especially for visceral fat, before and after bariatric surgery using computed tomography (CT) as a reference method. MATERIALS AND METHODS: This retrospective study included Korean patients with a BMI of ≥ 35, or ≥ 30 with metabolic comorbidities. All patients underwent bariatric surgery, and underwent BIA and CT evaluation before and 6 months after the operation. The skeletal muscle index (SMI) and visceral fat index (VFI), variables corrected for height, were compared between BIA and CT. RESULTS: Forty-eight patients (18 men, 30 women) demonstrated a mean weight loss of 27.5 kg. Significant decreases in both VFI and SMI were observed in both BIA and CT (all p<0.001), with greater VFI change compared to SMI (48.2% vs. 10.4% in CT, respectively). Both pre- and post-operative measurements of VFI and SMI were significantly correlated between BIA and CT (all p<0.05). However, the percent decrease was significantly correlated only with VFI (ρ=0.71, p<0.001). The Bland-Altman analysis showed that BIA underestimated VFI, with a greater degree of underestimation in subjects with higher VFI. CONCLUSION: Despite the underestimation of BIA in measuring visceral fat, BIA VFI was associated with CT VFI. The SMI values showed significant correlations before and after surgery, but not with the percent decrease. Our results suggest that BIA can be a reliable tool for measuring body composition, especially for visceral fat, after bariatric surgery.

Body Composition Change, Unhealthy Lifestyles and Steroid Treatment as Predictor of Metabolic Risk in Non-Hodgkin's Lymphoma Survivors.

Unhealthy lifestyle, as sedentary, unbalanced diet, smoking, and body composition change are often observed in non-Hodgkin's lymphoma (NHL) survivors, and could be determinant for the onset of cancer treatment-induced metabolic syndrome (CTIMetS), including abdominal obesity, sarcopenia, and insulin resistance. The aim of this study was to assess whether changes in body composition, unhealthy lifestyles and types of anti-cancer treatment could increase the risk of metabolic syndrome (MetSyn) and sarcopenia in long-term NHL survivors. We enrolled 60 consecutive NHL patients in continuous remission for at least 3 years. Nutritional status was assessed by anthropometry-plicometry, and a questionnaire concerning lifestyles and eating habits was administered. More than 60% of survivors exhibited weight gain and a change in body composition, with an increased risk of MetSyn. Univariate analysis showed a significantly higher risk of metabolic disorder in patients treated with steroids, and in patients with unhealthy lifestyles. These data suggest that a nutritional intervention, associated with adequate physical activity and a healthier lifestyle, should be indicated early during the follow-up of lymphoma patients, in order to decrease the risk of MetSyn's onset and correlated diseases in the long term.

Perioperative body composition changes in the multimodal treatment of gastrointestinal cancer.

Surgical resection and perioperative adjuvant therapy are widely accepted standard treatments for gastrointestinal cancer. However, body composition changes, such as weight loss and skeletal muscle loss, are unavoidable during these treatments. Several studies have shown that perioperative body composition changes are affected by multimodal treatment for gastrointestinal cancer. This review summarizes the background, current status, and future perspectives of perioperative body composition changes in the multimodal treatment of gastrointestinal cancer. Recent studies have described the body composition changes observed in the early period after surgery and during adjuvant therapy. Changes in the body composition might affect adjuvant chemotherapy toxicity after surgery and postoperative complications after neoadjuvant therapy. The mechanisms underlying body composition changes during multimodal therapy are multifactorial and include systemic inflammation, reduced nutrient intake, and physical inactivity. Several approaches have been tested to maintain the body composition, and especially prevent skeletal muscle wasting, during multimodal therapy. Although the ideal approach for managing body composition changes in gastrointestinal cancer patients remains unclear, recent studies support the combination of multiple approaches rather than a single approach.

Discordance Between Body Mass Index (BMI) and a Novel Body Composition Change Index (BCCI) as Outcome Measures in Weight Change Interventions.

OBJECTIVE: A general assumption is that the body mass index (BMI) reflects changes in fat mass (FM). However, it fails to distinguish the type of weight that is lost or gained-fat mass (FM) or fat-free mass (FFM). The BMI treats both changes the same although they have opposite health consequences. The objective of this study was to propose a more precise measure, a body composition change index (BCCI), which distinguishes between changes in FM and FFM, and this study compares it with using the BMI as an outcome measure. METHODS: Data were obtained from 3,870 subjects who had completed dual-energy x-ray absorptiometry (DEXA) total body scans at baseline and end-of-study when participating in a variety of weight-loss interventions. Since height remained constant in this adult cohort, changes in the BMI corresponded with scale weight changes (r = 0.994), allowing BMI changes to be converted to "lbs." to match the statistic used for calculation of the BCCI. The BCCI is calculated by scoring increases in FFM (lbs.) and decreases in FM (lbs.) as positive outcomes and scoring decreases in FFM and increases in FM as negative outcomes. The BCCI is the net sum of these calculations. Differences between scale weight changes and BCCI values were subsequently compared to obtain "discordance scores." RESULTS: Discordance scores ranged from 0.0 lbs. to >30.0 lbs. with a mean absolute value of between the two measures of 7.79 lbs. (99% confidence interval: 7.49-8.10, p <0.00001), SD = 7.4 lbs. Similar discordance scores were also found in subgroups of self-reported gender, ethnicity, and age. CONCLUSIONS: A significant difference of 7.79 lbs. was found between the BCCI and the BMI to evaluate the efficacy of weight loss interventions. If assessing changes in body composition is a treatment goal, use of the BMI could result in significantly erroneous conclusions.

ACCURACY OF ESTIMATING HUMAN BODY COMPOSITION CHANGES ON BI METHODS -WITH THE STUDY OF SERIAL MEASUREMENTS DURING THE WEIGHT / 体力科学

The purpose of this study was to investigate the accuracy of estimating human body composition changes using bioelectrical impedance (BI) methods during a weight-loss intervention. Subjects were forty-three obese men (age : 49.2±10.5 yr, BMI : 27.8±1.7 kg/m²) who completed a 14-week weight-loss intervention. In all subjects, fat mass (FM) and fat-free mass (FFM) were assessed by dual energy x-ray absorptiometry (DXA) as well as single- and multi-frequency BI methods (SBIM, MBIM) before and after the intervention. Resistance parameters were measured by SBIM and MBIM (SBIM : R₅₀ ; MBIM : R∞, R0, and Rfc). In nine subjects these variables were also measured at weeks 1 and 4. Weight decreased (P<0.05) by -8.0±3.2 kg during the intervention while FFM changes averaged -0.4±1.6 kg (DXA), -2.0±1.5 kg (SBIM), and -1.6±1.7 kg (MBIM). BI methods overestimated FFM before the intervention (before ; DXA : 54.4±4.8 kg, SBIM : 56.5±4.3 kg, MBIM : 55.9±4.5 kg). In nine subjects, FFM measured by SBIM (FFM_SBIM) and MBIM (FFM_MBIM) was similar to FFM measured by DXA(FFM_DXA)(after ; DXA : 54.6±5.4 kg, SBIM : 54.6±3.8 kg, MBIM : 54.6±4.1 kg), although BI methods overestimated the FFM before the intervention (before ; DXA : 54.9±5.1 kg, SBIM : 56.9±3.8 kg, MBIM : 56.3±4.4 kg). The ΔFM_SBIM and ΔFM_MBIM were highly correlated with the ΔFM_DXA(SBIM : r=0.87, MBIM : r=0.88). The ΔFFM_SBIM andΔFFM_MBIM were significantly correlated with the ΔFFM_DXA(SBIM : r=0.54, MBIM : r=0.49). The ΔR₅₀ and ΔRfc were also significantly correlated with the ΔFFM_DXA(R₅₀ : r=-0.63, Rfc : r=-0.48). These results suggest that during a weight-loss intervention, 1) BI methods and DXA provide similar estimates of human body composition change, although they overestimate FFM in obese men, and 2) changes of resistance parameters observed with BI methods may estimate human body composition change more accurately.