Defining cancer cachexia by WL over time may be limited as it does not capture skeletal muscle loss. Cross-sectional CT body composition analysis may improve early detection of muscle loss and patient participation in future cancer cachexia clinical trials.

BackgroundCancer is a systemic catabolic condition affecting skeletal muscle and fat. We aimed to determine whether cardiac atrophy occurs in this condition and assess its association with cardiac function, symptoms, and clinical outcomes.MethodsTreatment naïve metastatic non‐small cell lung cancer patients (n = 50) were assessed prior to and 4 months after commencement of carboplatin‐based palliative chemotherapy. Methods included echocardiography for left ventricular mass (LVM) and LV function [LV ejection fraction, global longitudinal strain (GLS), diastolic function], computed tomography to quantify skeletal muscle and total adipose tissue, Eastern Cooperative Oncology Group Performance Status (ECOG‐PS), validated questionnaires for dyspnoea and fatigue, plasma biomarkers, tumour response to therapy, and overall survival.ResultsDuring 112 ± 6 days, the median change in LVM was −8.9% [95% confidence interval (95% CI) −10.8 to −4.8, P < 0.001]. Quartiles of LVM loss were −20.1%, −12.9%, −4.8%, and +5.5%. Losses of muscle, adipose tissue, and LVM were frequently concurrent; LVM loss > median value was associated with loss of skeletal muscle [odds ratio (OR) = 4.5, 95% CI: 1.4–14.8, P=0.01] and loss of total adipose tissue (OR = 10.0, 95% CI: 2.7–36.7, P < 0.001). LVM loss was associated with decreased GLS (OR = 6.6, 95% CI: 1.9–22.7, P=0.003) but not with LV ejection fraction or diastolic function. In the population overall, plasma levels of C‐reactive protein (P=0.008), high sensitivity troponin T (hs‐TnT) (P=0.03), and galectin‐3 (P=0.02) increased over time, while N‐terminal pro B‐type natriuretic peptide and hs‐cTnI did not change over time. C‐reactive protein was the only biomarker associated with LVM loss but at the univariate level only. Independent predictors of LVM loss were prior weight loss (adjusted OR = 10.2, 95% CI: 2.2–46.9, P=0.003) and tumour progression (adjusted OR = 14.6, 95% CI: 1.4–153.9, P=0.02). LVM loss was associated with exacerbations of fatigue (OR = 6.6, 95% CI: 1.9–22.7, P=0.003), dyspnoea (OR = 9.3, 95% CI: 2.4–35.8, P<0.001), and deterioration of performance status (OR = 4.8, 95% CI: 1.3–18.3,P=0.02). Patients with concurrent loss of LVM, skeletal muscle, and fat were more likely to deteriorate in all three symptom domains and to have reduced survival (P=0.05).ConclusionsIntense LVM atrophy is associated with non‐small cell lung cancer‐induced cachexia. Loss of LVM was associated with emerging alterations of GLS, indicating subtle changes in left ventricular function. Longer term studies are needed to assess the full scope of cardiac atrophy and its impact. LVM atrophy arises in conjunction with losses of fat and skeletal muscle and is temporally associated with meaningful declines in performance status, worsening of fatigue, and dyspnoea, as well as poorer tumour response and decreased survival. The specific contribution of LVM atrophy to these outcomes requires further study.

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67 Background: Cancer cachexia is defined by skeletal muscle loss, with or without fat loss (Fearon et al 2011); however, inclusion criteria for cachexia clinical trials requires a defined weight loss over time rather than muscle loss. We hypothesized that cross sectional imaging may reveal the presence of cachexia otherwise obscured by fat mass changes. Methods: A retrospective analysis of longitudinal CT scans was performed in metastatic colorectal cancer (mCRC) patients screened for a cancer cachexia trial, which required ≥5% weight loss in the prior 6 mos. De-identified CT images were analyzed for total muscle, subcutaneous, and visceral fat cross-sectional areas (cm2) at the 3rd lumbar vertebra at baseline and up to 12 mos prior (Lieffers et al 2009). Logistic regression was used to test differences between patients with <5% vs ≥5% weight loss. Random intercept regression was used to evaluate significant trends in CT measures over time. Results: 42 mCRC patients were screened and 3(7%) enrolled. Patients were excluded for comorbidity/contraindication 14 (33%), excessive [>20%] weight loss 4 (9.5%), and insufficient [<5%] weight loss 19 (45%). For the <5% weight loss subset, there was a mean of 6.7 CT scans (SD=2.67) and of 9% (SD=5.4, min=0%, 25th percentile=4.9%) mean max muscle loss. Notably this group was simultaneously losing muscle (p=0.002) and gaining visceral adipose (p=0.007). For the ≥5% weight loss subset, there was a mean of 7.5 CT scans (SD=4.5) and 20% (SD=10.0, min=5.2%, 25th percentile =10.6) mean max muscle loss. Greater max muscle loss increased the odds of being in the ≥5% weight loss subset (OR=1.19, 95% CI: 1.06,1.33). This group also had a significant decrease in visceral adipose over time (p<0.001). Redefined inclusion criteria of ≥5% muscle loss would have included 14 of the 19 patients excluded because of <5% weight loss. Conclusions: Defining cancer cachexia as weight loss over time may be limited as it does not capture body composition changes and hinders trial accrual. Cross-sectional CT body composition analysis may improve early detection of muscle loss and improve trial accrual.

Background: Although body composition is an important determinant of pediatric health outcomes, we lack tools to routinely assess it in clinical practice. We de ne models to predict whole body skeletal muscle and fat composition, as measured by dual X-ray absorptiometry (DXA) or whole body magnetic resonance imaging (MRI), in pediatric oncology and healthy pediatric cohorts, respectively. Methods: Pediatric oncology patients (≥5 to ≤18 years) undergoing an abdominal CT were prospectively recruited for a concurrent study DXA scan. Cross-sectional areas of skeletal muscle and total adipose tissue at each lumbar vertebral level (L1-L5) were quanti ed and optimal linear regression models were de ned. Whole body and cross-sectional MRI data from a previously recruited cohort of healthy children (≥5 to ≤18 years) was analyzed separately.Results: Eighty pediatric oncology patients (57% male; age range 5.1-18.4y) were included. Crosssectional areas of skeletal muscle and total adipose tissue at lumbar vertebral levels (L1-L5) were correlated with whole body lean soft tissue mass (LSTM) (R 2 =0.896-0.940) and fat mass (FM) (R 2 =0.874-0.936) (p<0.001). Linear regression models were improved by the addition of height for prediction of LSTM (adjusted R 2 =0.946-0.971; p<0.001) and by the addition of height and sex (adjusted R 2 =0.930-0.953) (p<0.001)) for prediction of whole body FM. High correlation between lumbar cross-sectional tissue areas and whole body volumes of skeletal muscle and fat, as measured by whole body MRI, was con rmed in an independent cohort of 73 healthy children. Conclusion:Regression models can predict whole body skeletal muscle and fat in pediatric patients utilizing cross-sectional abdominal images.