Enhancing TAVI Patient Evaluation: A User-Friendly Tool for CT-Derived Body Composition Assessment

Background CT-derived measures of body composition have been shown to have prognostic value in patients with cancer. However, few studies have compared these observations across tumor types and stages of disease. The aim of the present study was to compare body composition measures between two types of cancers, i.e. colorectal cancer (CRC), which is less inflammatory and patients maintain body composition over a longitudinal study period, whereas lung cancer (LC) is proinflammatory and patients lose more fat and muscle mass using a standard methodology. Methods Clinicopathological characteristics, including those pertaining to nutritional risk/status and systemic inflammation in patients with colorectal cancer (CRC, n = 1047) and lung cancer (LC, n = 662), were compared. The CT image at L3 was used to assess body composition. Comparison of these cohorts was carried out using the chi-square test. Binary logistic regression analysis was performed to assess the impact of clinico-pathological variables on body composition, and scatter plots were used to examine the relationship between body mass index (BMI) and CT-derived measures of body composition. Results According to CT-derived body composition, high subcutaneous (SFI) and visceral fat index (VFI) were common (>70%) in both CRC and LC. Also, low skeletal muscle index (SMI) and density (SMD) were approximately 40–50% and 60–70% in both CRC and LC. Compared with CRC, patients with LC had a higher American Society of Anaesthesia (ASA) (P < 0.001), Malnutrition Universal Screening Tool (MUST) (P < 0.001), modified frailty index (mFI) (P < 0.001), modified Glasgow Prognostic Score (mGPS) (P < 0.001), and neutrophil lymphocyte ratio (NLR) (P < 0.001) scores. On binary logistic regression analysis, MUST, mFI, and NLR were predictors of subcutaneous adiposity (P < 0.05); type of cancer, MUST, and mFI were predictors of visceral obesity (P < 0.001); age, type of cancer, MUST, and mGPS were predictors of low SMI (P < 0.001); and age, type of cancer, mFI, and mGPS were predictors of low SMD (P < 0.05). There was a similar relationship between BMI and other measures of CT-derived body composition across two types of cancers. Conclusion Obesity and low skeletal muscle mass were common in both CRC and LC cohorts despite large differences in comorbidity, nutritional risk, systemic inflammation, and survival, even when normalized for TNM stage. These observations would support the hypothesis that, although prognostic, CT derived body composition analysis primarily reflects patient constitution rather than the effect of tumor stage in patients with cancer. The systemic inflammatory response, as evidenced by mGPS, can be considered as an important therapeutic target and loss of muscle mass in patients with advanced cancer is related to the systemic inflammatory response.

Within colorectal cancer, the systemic inflammatory response (SIR) and CT-derived body composition, particularly the loss of lean muscle mass, are independently associated with oncological outcomes; however, no study has included both non-metastatic and metastatic disease. The present study analyses the association between body composition, mode of presentation, SIR and survival in patients with TNM I-IV colon cancer. Patients diagnosed with colon cancer from 2011 to 2014 were identified. The SIR was stratified using systemic inflammatory grade (SIG). Staging CT scans were used to define body composition: subcutaneous fat index (SFI), visceral fat area (VFA), skeletal muscle index (SMI) and skeletal muscle density (SMD). The effect of SIG and body composition on mode of presentation and 3-year overall survival (3-yr OS) was analysed. One thousand one hundred forty-six patients were identified; 14%/38%/40%/8% had TNM Stage I/II/III/IV colon cancer, respectively. Patients were predominantly aged 65+(63%), male (52%) and BMI>25 (62%). 79%74% had a high SFI/VFA, and 56%/62% had a low SMI/SMD, respectively. Abnormal body composition was prevalent across all disease stages and associated with TNM stage-high SFI in 87%/76%/81%/68% (P<0.001), high VFA in 79%/73%/75%/67% (P=0.189), low SMI in 43%/60%/55%/68% (P<0.001) and low SMD in 55%/65%/61%/67% (P=0.094) of TNM I/II/III/IV disease, respectively. Body composition was associated with SIG-high SFI in 83%/80%/77%/78%/66% (P=0.004), high VFA in 78%/78%/70%/63%/61% (P=0.002), low SMI in 48%/52%/62%/62%/79% (P<0.001) and low SMD in 56%/60%/62%/70%/76% (P<0.001) of patients with SIG 0/1/2/3/4, respectively. After adjustment for other factors, increased SIG (OR 1.95), visceral obesity (OR 0.65) and low SMI (OR 1.61) were associated with emergency presentation. In TNM Stage II colon cancer, low SMI and low SMD were associated with worse 3-yr OS (92% vs 87%, P<0.001 and 96% vs 85%, P<0.001, respectively). In TNM Stage III, a trend was seen between low SMI and SMD and 3-yr OS (77% vs 73%, P=0.091 and 76% vs 75%, P=0.034, respectively). In TNM Stage IV disease, low SMI was associated with 3-yr OS (43% vs 16%, P<0.001). A trend, albeit not of significance, was seen between low SMD and 3-yr OS (32% vs 21%, P=0.366). The present results show that abnormal body composition is prevalent across TNM I-IV colon cancer and associated with TNM stage and SIG. Body composition is independently associated with emergency presentation and long-term survival. Further research is required to analyse whether interventions including structured exercise programmes or attenuation of the SIR have an effect on CT-derived body composition and oncological outcomes.

With weight loss increasingly occurring against a background of obesity across a variety of advanced cancers, there has been increasing interest in computed tomography (CT)-derived body composition analysis. Various imaging software packages and thresholds are commonly in use in CT-derived body composition analysis. This review discusses the current research in field of body composition with emphasis on the information required for such measurements to be taken into routine clinical practice. CT is widely used for tumour staging in patients with cancer. Over the last decade, this imaging modality has been exploited to make measurements of body composition. Using a common landmark (L3) several different thresholds have been developed to stratify adipose and muscle tissue areas according to age, sex and BMI and their relationship with survival. A significant relationship between CT-derived body composition and clinical outcomes has been shown in different tumour types and geographical locations. However, there is considerable variation with methods, thresholds and muscle groups used for analysis. Therefore, there would appear to be a need to develop reliable methodology and population-specific reference ranges to guide clinical interpretation and enable routine clinical use. There has been an explosion of clinical research interest in CT-derived body composition analysis. Such body composition analysis provides important host phenotype information which has prognostic value. For CT-derived body composition to be fit for use in routine clinical practice, there is need for universally accepted terminology, software, muscle group selection, prognostic thresholds to standardize such body composition analysis.

CT-derived body composition (BC) metrics are associated with mortality in cancer patients at baseline and during treatment. However, the relationship between CT-derived BC metrics and blood biomarkers, and their independent prognostic value, has been scarcely evaluated. This study assessed the correlation between BC metrics and blood test results, and whether longitudinal changes independently predict mortality in patients receiving immunotherapy for solid tumors. We included patients treated with immunotherapy for non-small cell lung cancer (NSCLC), melanoma, or renal cell carcinoma (RCC) between 2017 and 2024, who had baseline and follow-up CT scans. Patients could be recorded more than once as distinct treatment events if there was a treatment break of ≥180 days, or a new drug combination. BC was analyzed using fully automated AI software (CompoCT@), measuring skeletal muscle (SM), including healthy muscle (HM) and steatotic muscle (StM); subcutaneous layer (SCL), comprising subcutaneous fat (SCF) and subcutaneous edema (SCE); and visceral fat (VF) at the L3 vertebral level. BC indices (e.g. SMI) were calculated by dividing the corresponding area (cm2) by the patient’s height squared (m2). Laboratory data included albumin, LDH, CRP, hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR) and white blood cell count (WBC). The cohort included 418 events from 392 patients (mean (median) age 66.2 (67) years; 66.5% male, 71.6% NSCLC, 14.2% melanoma, and 14.2% RCC). No significant correlations were observed between blood tests and CT BC metrics. Baseline values of albumin, Hb and NLR significantly correlated with mortality while baseline CT metrics did not. However, longitudinal percentage decrease in SMI (1/HR=25), HMI (1/HR=2.5) and SCFI (1/HR≈8.3) and increases in SCEI (HR=1.69), were all significantly associated with mortality (p&amp;lt;0.001). Changes in LDH, WBC, NLR, and Hb also correlated with mortality (HR=1.7;1.61;1.2;0.28 respectively), whereas changes in albumin levels did not. Three distinct multivariate models were constructed to evaluate the prognostic performance of different variable sets. The first model combined CT-derived BC metrics with blood biomarkers and demonstrated the highest predictive accuracy (concordance index [CI] = 0.78). The second model included only BC metrics (CI = 0.72), while the third relied solely on blood biomarkers (CI = 0.69). In the combined model, higher mortality was significantly associated with NSCLC diagnosis, longitudinal changes in CT-derived BC metrics, including %∆StMI, %∆HMI, and %∆SCFI, as well as baseline albumin values and changes in LDH levels (p &amp;lt; 0.05 for all). In patients with solid tumors receiving immunotherapy, longitudinal CT-based changes in muscle and fat were more predictive of mortality than traditional sarcopenia-related blood biomarkers. Opportunistic use of CT data, extracted via fully automated AI algorithms, may enhance clinical management decisions, by offering additive value to conventional blood tests related to muscle wasting and systemic inflammation. Citation Format: Shlomit Tamir, Hilla Vardi Behar, Ronen Tal, Ruth Tal Hasper, Mor Armoni, Hadar Pratt Aloni, Rotem Or Ad, Hillary Voet, Eli Atar, Ahuva Grubstein, Salomon Stemmer, Gal Markel. Dynamics in automatic CT based body composition and blood biomarkers in predicting mortality on immune therapy treated solid malignancy patients [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A064.

BACKGROUND Fast Healthcare Interoperability Resources (FHIR) is a widely used standard for storing and exchanging health care data. At the same time, image-based artificial intelligence (AI) models for quantifying relevant body structures and organs from routine computed tomography (CT)/magnetic resonance imaging scans have emerged. The missing link, simultaneously a needed step in advancing personalized medicine, is the incorporation of measurements delivered by AI models into an interoperable and standardized format. Incorporating image-based measurements and biomarkers into FHIR profiles can standardize data exchange, enabling timely, personalized treatment decisions and improving the precision and efficiency of patient care. OBJECTIVE This study aims to present the synergistic incorporation of CT-derived body organ and composition measurements with FHIR, delineating an initial paradigm for storing image-based biomarkers. METHODS This study integrated the results of the Body and Organ Analysis (BOA) model into FHIR profiles to enhance the interoperability of image-based biomarkers in radiology. The BOA model was selected as an exemplary AI model due to its ability to provide detailed body composition and organ measurements from CT scans. The FHIR profiles were developed based on 2 primary observation types: Body Composition Analysis (BCA Observation) for quantitative body composition metrics and Body Structure Observation for organ measurements. These profiles were structured to interoperate with a specially designed Diagnostic Report profile, which references the associated Imaging Study, ensuring a standardized linkage between image data and derived biomarkers. To ensure interoperability, all labels were mapped to SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms) or RadLex terminologies using specific value sets. The profiles were developed using FHIR Shorthand (FSH) and SUSHI, enabling efficient definition and implementation guide generation, ensuring consistency and maintainability. RESULTS In this study, 4 BOA profiles, namely, Body Composition Analysis Observation, Body Structure Volume Observation, Diagnostic Report, and Imaging Study, have been presented. These FHIR profiles, which cover 104 anatomical landmarks, 8 body regions, and 8 tissues, enable the interoperable usage of the results of AI segmentation models, providing a direct link between image studies, series, and measurements. CONCLUSIONS The BOA profiles provide a foundational framework for integrating AI-derived imaging biomarkers into FHIR, bridging the gap between advanced imaging analytics and standardized health care data exchange. By enabling structured, interoperable representation of body composition and organ measurements, these profiles facilitate seamless integration into clinical and research workflows, supporting improved data accessibility and interoperability. Their adaptability allows for extension to other imaging modalities and AI models, fostering a more standardized and scalable approach to using imaging biomarkers in precision medicine. This work represents a step toward enhancing the integration of AI-driven insights into digital health ecosystems, ultimately contributing to more data-driven, personalized, and efficient patient care.

Fast Healthcare Interoperability Resources (FHIR) is a widely used standard for storing and exchanging health care data. At the same time, image-based artificial intelligence (AI) models for quantifying relevant body structures and organs from routine computed tomography (CT)/magnetic resonance imaging scans have emerged. The missing link, simultaneously a needed step in advancing personalized medicine, is the incorporation of measurements delivered by AI models into an interoperable and standardized format. Incorporating image-based measurements and biomarkers into FHIR profiles can standardize data exchange, enabling timely, personalized treatment decisions and improving the precision and efficiency of patient care. This study aims to present the synergistic incorporation of CT-derived body organ and composition measurements with FHIR, delineating an initial paradigm for storing image-based biomarkers. This study integrated the results of the Body and Organ Analysis (BOA) model into FHIR profiles to enhance the interoperability of image-based biomarkers in radiology. The BOA model was selected as an exemplary AI model due to its ability to provide detailed body composition and organ measurements from CT scans. The FHIR profiles were developed based on 2 primary observation types: Body Composition Analysis (BCA Observation) for quantitative body composition metrics and Body Structure Observation for organ measurements. These profiles were structured to interoperate with a specially designed Diagnostic Report profile, which references the associated Imaging Study, ensuring a standardized linkage between image data and derived biomarkers. To ensure interoperability, all labels were mapped to SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) or RadLex terminologies using specific value sets. The profiles were developed using FHIR Shorthand (FSH) and SUSHI, enabling efficient definition and implementation guide generation, ensuring consistency and maintainability. In this study, 4 BOA profiles, namely, Body Composition Analysis Observation, Body Structure Volume Observation, Diagnostic Report, and Imaging Study, have been presented. These FHIR profiles, which cover 104 anatomical landmarks, 8 body regions, and 8 tissues, enable the interoperable usage of the results of AI segmentation models, providing a direct link between image studies, series, and measurements. The BOA profiles provide a foundational framework for integrating AI-derived imaging biomarkers into FHIR, bridging the gap between advanced imaging analytics and standardized health care data exchange. By enabling structured, interoperable representation of body composition and organ measurements, these profiles facilitate seamless integration into clinical and research workflows, supporting improved data accessibility and interoperability. Their adaptability allows for extension to other imaging modalities and AI models, fostering a more standardized and scalable approach to using imaging biomarkers in precision medicine. This work represents a step toward enhancing the integration of AI-driven insights into digital health ecosystems, ultimately contributing to more data-driven, personalized, and efficient patient care.

Body Composition: Assessment, Regulation, and Emerging Techniques

Loss of skeletal muscle mass and systemic inflammation may offer prognostic value in patients with abdominal aortic aneurysm (AAA). The longitudinal progression of abnormal body composition parameters and their determinants is poorly reported. Statins are widely used medications that improve the prognosis of cardiovascular disease and interact with both muscle tissue and systemic inflammation. The present study aimed to describe the association between statin therapy and both pre-operative and longitudinal CT-derived body composition in patients undergoing elective intervention for AAA. A total of 756 consecutive patients undergoing elective intervention for AAA at three centres were retrospectively recruited. Body composition analysis was performed on pre-operative and follow-up CTs at L3 to generate subcutaneous adipose tissue index, visceral adipose tissue index and skeletal muscle index and density (SMI and SMD). Systemic inflammation was assessed using the systemic inflammatory grade. A total of 756 patients (702 [93%] males, median [interquartile range, IQR] age 73.0 [11.0] years) were included, with a median (IQR) follow-up of 67.0 (32) months and 235 deaths during the follow-up period. There were 582 patients (77%) receiving statin therapy and 174 patients (23%) not receiving statin therapy. Follow-up CTs were available for 273 patients. From pre-operative to follow-up CTs, there was a decrease in median SMI (P<0.001) and SMD (P<0.001) and an increase in the comparative prevalences of low SMI (43% vs. 50%, P<0.01) and low SMD (64% vs. 88%, P<0.001). There were no differences in baseline clinicopathological characteristics, systemic inflammation or pre-operative CT-derived body composition parameters between patients with and without >10% loss of skeletal muscle mass. In patients with ≤10% loss of SMI, mean (95% confidence interval) survival was 91.6 (87.2-95.9) months versus 89.3 (80.4-98.2) months in patients with >10% loss of SMI (P=0.58). Patients receiving statin therapy had a higher American Society of Anesthesiologists grade (P<0.001), a higher body mass index (BMI) (P<0.05) and a greater prevalence of normal pre-operative SMI (P<0.001). In patients with AAA, skeletal muscle mass and density appear to progressively decline despite treatment of AAA, though specific determinants of this are uncertain, and statin use does not appear to predispose to either muscle loss or preservation. Statin therapy appears to be associated with a lower rate of pre-operative low skeletal muscle mass, despite greater comorbidity and BMI. Further investigation of the progressive changes in muscle mass and quality, statin therapy and systemic inflammation is warranted.

Background: Obesity is associated with higher risk of breast cancer (BC)-related death, all-cause mortality, and recurrence. While used to estimate body fat, body mass index (BMI) is insensitive to body fat distribution and lean muscle mass. We hypothesized that BMI categories (BMI 18.5-24.9 [healthy], 25-29.9 [overweight], and ≥30 kg/m2 [obesity]) would not correlate with CT-derived body composition. Methods: We retrospectively identified 180 patients diagnosed with new or recurrent Stage I-IV BC who presented to Johns Hopkins from 2015-2018 and were part of an institutional database. We extracted demographics, diagnosis date, cancer characteristics, BMI, and CT abdomen pelvis scans from the medical record. Baseline BMI was defined as the closest measurement to diagnosis between 1 year prior and 1 month after diagnosis. Baseline CT was within 6 months of and closest to the baseline BMI date. Fully automated deep-learning algorithms identified L1 and performed muscle and adipose tissue segmentation and quantification. Body composition data included: cross-sectional areas (CSA) of subcutaneous, visceral, and total adipose tissue; the mean, median, and standard deviation of the average of the muscle attenuation (density); and the body wall musculature CSA. ANOVA tests assessed associations between body composition, BMI, and menopausal status. Results: Among 180 patients, 136 (76%) had early-stage BC, and 44 (24%) had metastatic BC. Most patients were women (98%) and non-Hispanic (94%). Patients identified as White (57%), Black (29%), Asian (6%), and Other Race (8%). Sixty percent were post-menopausal. Hormone receptor-positive, HER2-positive, and triple negative subtypes were 56%, 19% and 22%, respectively. At diagnosis, 24% had a healthy BMI while 36% and 41% had overweight and obesity, respectively. All body composition measures were significantly associated with BMI (Table 1). As BMI increased, the average body wall musculature and subcutaneous, visceral, and total adipose tissue CSAs increased while the average muscle attenuation decreased. Specifically, the average visceral adipose tissue CSAs were 2.7 and 4.0-fold larger for overweight and obesity compared to that of the healthy BMI group, respectively, while their average muscle attenuations were 62.4% and 40.6% of the healthy BMI group. Postmenopausal women had 1.5-fold higher visceral adipose tissue CSA and 44.7% less average muscle attenuation compared to premenopausal women. Conclusion: Body composition correlates with all BMI categories in primarily white, non-Hispanic women with breast cancer. As BMI increases, body composition changes reliably, with increased subcutaneous and visceral adipose tissue, increased muscle area, and decreased muscle density. Muscle density, not size/area, is more associated with muscle performance, which can relate to functional status and outcomes. Despite validity concerns, BMI is an accessible and economical surrogate for body adiposity in patients with breast cancer. Postmenopausal status was also associated with greater visceral and total adiposity and lower muscle density. Further investigation is needed to confirm these findings. Table 1: Correlation between body composition, BMI, and menopausal status in patients with breast cancer All measures of body composition were significantly associated with BMI. Visceral adipose tissue increased and muscle density decreased as BMI increased and with post-menopausal status. Citation Format: Terrence Tsou, Tingchang Wang, Amanda Blackford, Ronald Summers, Vered Stearns, Jennifer Sheng. Body composition correlates with BMI and menopausal status in patients with breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-11-09.

Background: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual’s overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy. Methods: We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used. Results: The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738–0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594–0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783–0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated. Conclusions: Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.

Birth weight percentiles provide limited information on qualitative infant growth. Body composition provides estimates of fat mass, fat-free mass, and body fat percentage (adiposity). We sought to implement assessment of body composition at birth into clinical practice using a validated anthropometric equation and to evaluate measurement reliability. Body composition was incorporated into newborn nursery admission procedure. Body fat percentage derived from skinfold measurements performed by clinical nurses were compared to a historical database of similar measurements performed on newborns by experienced research staff. Body Mass Index (BMI) and Ponderal Index (PI) were used as surrogates for adiposity. Comparison of correlations between groups assessed measurement reliability. P < 0.05 was considered significant. Nine hundred and ninety-one infants had body composition evaluated. Correlations were similar between BMI and %BF for measurements performed by research and clinical nurses (r2 = 0.82 versus r2 = 0.80; P = 0.142 for the difference between correlation coefficients) demonstrating good reliability. Similar results were found using PI (r2 = 0.58 versus r2 0.53; P = 0.105). Body composition can be assessed at birth using a validated anthropometric equation. Measurements performed by clinical RNs were found to be reliable, allowing for a qualitative measure of growth beyond birth weight. Assessment of neonatal body composition at birth can be implemented into routine clinical practice using an anthropometric equation to estimate fat free-mass, fat mass, and percentage body fat. It provides a detailed, reproducible protocol to incorporate into routine practice. Assessment of fat mass, fat-free mass, and adiposity at birth allows for a qualitative measure of intrauterine growth beyond birth weight. Routine assessment of body composition provides a foundation for longitudinal follow-up of metabolic health in infancy and childhood.

CT-derived body composition metrics such as skeletal muscle index (SMI), pectoralis muscle index (PMI), muscle radiation attenuation (MRA), and visceral fat area (VFA) have emerged as promising prognostic tools in lung transplant candidates. This systematic review evaluates their utility in pre- and post-transplant risk assessment and rehabilitation through a detailed literature search of PubMed, Google Scholar, and Excerpta Medica Database (EMBASE), identifying 21 relevant studies. Reduced muscular area (SMI) and quality (MRA) were associated with increased mortality, prolonged mechanical ventilation, and extended ICU stay, while low MRA correlated with higher rates of infection and graft dysfunction. Elevated VFA was linked to increased metabolic complications, and composite phenotypes such as sarcopenic obesity conferred additional risk. These findings suggest that CT-derived body composition metrics provide valuable, objective insights for transplant risk stratification and recovery. Standardized imaging protocols and integration with functional assessments are needed to support their widespread clinical adoption and optimize outcomes for high-risk lung transplant candidates.

Abstract: Over the years bioelectrical impedance assay (BIA) has gained popularity in the assessment of body composition. However, equations for the prediction of whole body composition use whole body BIA. This study attempts to evaluate the usefulness of segmental BIA in the assessment of whole body composition. A cross sectional descriptive study was conducted at the Professorial Paediatric Unit of Lady Ridgeway Hospital, Colombo, involving 259 (M/F:144/115) 5 to 15 year old healthy children. The height, weight, total and segmental BIA were measured and impedance indices and specific resistivity for the whole body and segments were calculated. Segmental BIA indices showed a significant association with whole body composition measures assessed by total body water (TBW) using the isotope dilution method (D2O). Impedance index was better related to TBW and fat free mass (FFM), while specific resistivity was better related to the fat mass of the body. Regression equations with different combinations of variables showed high predictability of whole body composition. Results of this study showed that segmental BIA can be used as an alternative approach to predict the whole body composition in Sri Lankan children. Doi: http://dx.doi.org/10.4038/jnsfsr.v40i3.4697 J.Natn.Sci.Foundation Sri Lanka 2012 40 (3):231-239

Chronic obstructive pulmonary disease (COPD) is a complex syndrome and an important public health challenge. Although defined as a chronic inflammatory respiratory disease, COPD is heterogeneous, being characterized by a number of systemic consequences and co-morbidities, which contribute to disease severity. Specifically, nutritional disorders (i.e. malnutrition) and nutritionrelated conditions (i.e. muscle dysfunction) are highly prevalent extra pulmonary manifestations of COPD, associated with important consequences for risk assessment stratification and management of the disease. General aim of this thesis was to investigate the occurrence of alterations of body composition and its relationship with muscle strength. More specifically, five studies on COPD patients had been carried out in order to systematically review the use of bioelectrical impedance analysis (BIA) for the assessment of body composition; to evaluate the prevalence of malnutrition and sarcopenia and their relationship with functional parameters; to compare BIA variables between COPD patients and controls and to study the association of muscle strength with body composition estimates and BIA variables. As a final point, this thesis aimed to explore the amount of visceral adipose tissue located in the abdominal region using dual-energy x-ray absorptiometry and to determine its relation with respiratory parameters and other indices of body composition. In conclusion, this thesis provides a detailed overview of the assessment of nutritional status and body composition in COPD patients, especially in relation with respiratory function and muscle strength, bringing to light the need for prevention strategies and suggesting possible tools for the implementation of personalized approaches for COPD patients.

Accurate methods for assessing body composition in subjects with obesity and anorexia nervosa (AN) are important for determination of metabolic and cardiovascular risk factors and to monitor therapeutic interventions. The purpose of our study was to assess the accuracy of dual-energy X-ray absorptiometry (DXA) for measuring abdominal and thigh fat, and thigh muscle mass in premenopausal women with obesity, AN, and normal weight compared to computed tomography (CT). In addition, we wanted to assess the impact of hydration on DXA-derived measures of body composition by using bioelectrical impedance analysis (BIA). We studied a total of 91 premenopausal women (34 obese, 39 with AN, and 18 lean controls). Our results demonstrate strong correlations between DXA- and CT-derived body composition measurements in AN, obese, and lean controls (r = 0.77-0.95, P < 0.0001). After controlling for total body water (TBW), the correlation coefficients were comparable. DXA trunk fat correlated with CT visceral fat (r = 0.51-0.70, P < 0.0001). DXA underestimated trunk and thigh fat and overestimated thigh muscle mass and this error increased with increasing weight. Our study showed that DXA is a useful method for assessing body composition in premenopausal women within the phenotypic spectrum ranging from obesity to AN. However, it is important to recognize that DXA may not accurately assess body composition in markedly obese women. The level of hydration does not significantly affect most DXA body composition measurements, with the exceptions of thigh fat.