Adipose Tissue Distribution and Survival Among Women with Nonmetastatic Breast Cancer.

ObjectivePrevious studies of breast cancer survival have not considered specific depots of adipose tissue such as subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT).MethodsThis study assessed these relationships among 3,235 women with stage II and III breast cancer diagnosed between 2005 and 2013 at Kaiser Permanente Northern California and between 2000 and 2012 at Dana Farber Cancer Institute. SAT and VAT areas (in centimeters squared) were calculated from routine computed tomography scans within 6 (median: 1.2) months of diagnosis, covariates were collected from electronic health records, and vital status was assessed by death records. Hazard ratios (HRs) and 95% CIs were estimated using Cox regression.ResultsSAT and VAT ranged from 19.0 to 891 cm2 and from 0.484 to 454 cm2 , respectively. SAT was related to increased risk of death (127-cm2 increase; HR [95% CI]: 1.13 [1.02-1.26]), but no relationship was found with VAT (78.18-cm2 increase; HR [95% CI]: 1.02 [0.91-1.14]). An association with VAT was noted among women with stage II cancer (stage II: HR: 1.17 [95% CI: 0.99-1.39]; stage III: HR: 0.90 [95% CI: 0.76-1.07]; P interaction < 0.01). Joint increases in SAT and VAT were associated with mortality above either alone (simultaneous 1-SD increase: HR 1.19 [95% CI: 1.05-1.34]).ConclusionsSAT may be an underappreciated risk factor for breast cancer-related death

Change in longitudinal trends in sleep quality and duration following breast cancer diagnosis: results from the Women's Health Initiative

Breast cancer survivors frequently report sleep problems, but little research has studied sleep patterns longitudinally. We examined trends in sleep quality and duration up to 15 years before and 20 years after a diagnosis of breast cancer, over time among postmenopausal women participating in the Women's Health Initiative (WHI). We included 12,098 participants who developed invasive breast cancer after study enrollment. A linear mixed-effects model was used to determine whether the time trend in sleep quality, as measured by the WHI Insomnia Rating Scale (WHIIRS), a measure of perceived insomnia symptoms from the past 4 weeks, changed following a cancer diagnosis. To examine sleep duration, we fit a logistic regression model with random effects for both short (<6 h) and long (≥9 h) sleep. In addition, we studied the association between depressive symptoms and changes in WHIIRS and sleep duration. There was a significantly slower increase in the trend of WHIIRS after diagnosis (β = 0.06; p = 0.03), but there were non-significant increases in the trend of the probability of short or long sleep after diagnosis. The probability of depressive symptoms significantly decreased, though the decrease was more pronounced after diagnosis (p < 0.01). Trends in WHIIRS worsened at a relatively slower rate following diagnosis and lower depression rates may explain the slower worsening in WHIIRS. Our findings suggest that over a long period of time, breast cancer diagnosis does not adversely affect sleep quality and duration in postmenopausal women compared to sleep pre-diagnosis, yet both sleep quality and duration continue to worsen over time.WHI - National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services [HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, HHSN268201600004C]; Ohio State University Susan G Komen Graduate Trainee Program [GTDR15334082]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Identifying Metabolomic Profiles of Insulinemic Dietary Patterns

The food-based empirical dietary index for hyperinsulinemia (EDIH) score assesses the insulinemic potential of diet. This cross-sectional study evaluated associations between EDIH scores from food frequency questionnaires with c-peptide concentrations and with 448 metabolites, from fasting plasma samples, in multivariable linear regression analyses. Metabolites were measured with liquid chromatography tandem mass spectroscopy. Using a robust two-stage study design, discovery of metabolite associations was conducted among 1109 Women’s Health Initiative (WHI) Hormone Therapy (HT) trial participants and results replicated in an independent dataset of 810 WHI Observational Study (OS) participants. In both discovery and replication datasets, statistical significance was based on the false-discovery rate adjusted P \u3c 0.05. In the multivariable-adjusted analyses, EDIH was significantly associated with c-peptide concentrations among 919 women (HT & OS) with c-peptide data. On average, c-peptide concentrations were 18% higher (95% CI, 6%, 32%; P-trend \u3c 0.0001) in EDIH quintile 5 compared to quintile 1. Twenty-six metabolites were significantly associated with EDIH in the discovery dataset, and 19 of these were replicated in the validation dataset. Nine metabolites were found to decrease in abundance with increasing EDIH scores and included: C14:0 CE, C16:1 CE, C18:1 CE, C18:3 CE, C20:3 CE, C20:5 CE, C36:1 PS plasmalogen, trigonelline, and eicosapentanoate, whereas the 10 metabolites observed to increase with increasing EDIH scores were: C18:2 SM, C36:3 DAG, C36:4 DAG-A, C51:3 TAG, C52:3 TAG, C52:4, TAG, C54:3 TAG, C54:4 TAG, C54:6 TAG, and C10:2 carnitine. Cholesteryl esters, phospholipids, acylglycerols, and acylcarnitines may constitute circulating metabolites that are associated with insulinemic dietary patterns

The evolution of body composition in oncology—epidemiology, clinical trials, and the future of patient care: facts and numbers

Abstract There is growing interest from the oncology community to understand how body composition measures can be used to improve the delivery of clinical care for the 18.1 million individuals diagnosed with cancer annually. Methods that distinguish muscle from subcutaneous and visceral adipose tissue, such as computed tomography (CT), may offer new insights of important risk factors and improved prognostication of outcomes over alternative measures such as body mass index. In a meta‐analysis of 38 studies, low muscle area assessed from clinically acquired CT was observed in 27.7% of patients with cancer and associated with poorer overall survival [hazard ratio: 1.44, 95% CI: 1.32–1.56]. Therapeutic interventions such as lifestyle and pharmacotherapy that modify all aspects of body composition and reduce the incidence of poor clinical outcomes are needed in patients with cancer. In a meta‐analysis of six randomized trials, resistance training exercise increased lean body mass assessed from dual‐energy X‐ray absorptiometry [mean difference (MD): +1.07 kg, 95% CI: 0.76–1.37; P < 0.001] and walking distance [MD: +143 m, 95% CI: 70–216; P < 0.001] compared with usual care control in patients with non‐metastatic cancer. In a meta‐analysis of five randomized trials, anamorelin (a ghrelin agonist) significantly increased lean body mass [MD: +1.10 kg, 95% CI: 0.35–1.85; P = 0.004] but did not improve handgrip strength [MD: 0.52 kg, 95% CI: −0.09–1.13; P = 0.09] or overall survival compared with placebo [HR: 0.99, 95% CI: 0.85–1.14; P = 0.84] in patients with advanced or metastatic cancer. Early screening to identify individuals with occult muscle loss, combined with multimodal interventions that include lifestyle therapy with resistance exercise training and dietary supplementation combined with pharmacotherapy, may be necessary to provide a sufficient stimulus to prevent or slow the cascade of tissue wasting. Rapid, cost‐efficient, and feasible methods to quantify muscle and adipose tissue distribution are needed if body composition assessment is to be integrated into large‐scale clinical workflows. Fully automated analysis of body composition from clinically acquired imaging is one example. The study of body composition is one of the most provocative areas in oncology that offers tremendous promise to help patients with cancer live longer and healthier lives

Screening for low muscularity in colorectal cancer patients: a valid, clinic‐friendly approach that predicts mortality

Abstract Background Low skeletal muscle quantified using computed tomography (CT) scans is associated with morbidity and mortality among cancer patients. However, existing methods to assess skeletal muscle from CT are time‐consuming, expensive, and require training. Clinic‐friendly tools to screen for low skeletal muscle in cancer patients are urgently needed. Methods We included 807 scans from non‐metastatic colorectal cancer patients. With the digital ruler available in most radiological software, we implemented an abbreviated method to assess skeletal muscle area at the third lumbar vertebra (L3), which consisted of assessing the height and width of the psoas and paraspinal muscles and computing their combined ‘linear area’ in centimetres squared (cm2). A subset of CT scans was assessed twice by two analysts to compute intra‐rater and inter‐rater reliability. We derived cut‐points for ‘low’ linear area using optimal stratification and then calculated the sensitivity and specificity of these cut‐points relative to standard methods (total L3 cross‐sectional area assessed with Slice‐O‐Matic research software). We further evaluated the association of low linear area with death from any cause after colorectal cancer diagnosis in Cox proportional hazards models adjusting for demographics, smoking, body mass index category, and tumour characteristics. Results The linear area was highly correlated with total cross‐sectional area assessed using standard methods [r = 0.92; 95% confidence interval (CI): 0.91, 0.93] overall and within subgroups defined by age, sex, and body mass index group. Intra‐rater and inter‐rater reliability were equally high (both intra‐class correlations = 0.98). Cut‐points for low linear area were sensitive (0.75; 95% CI: 0.70, 0.80) and specific (0.77; 95% CI: 0.73, 0.80) for identifying low skeletal muscle relative to the standard of total L3 cross‐sectional area. The hazard ratio and 95% CI for death associated with a low linear area were hazard ratio = 1.66; 95% CI: 1.22, 2.25. Conclusions Clinic‐friendly methods that assess linear area from CT scans are an accurate screening tool to identify low skeletal muscle among non‐metastatic colorectal cancer patients. These linear measures are associated with mortality after colorectal cancer, suggesting they could be clinically useful both to improve prognostication and to provide a practical screening tool to identify cancer patients who require nutrition or exercise intervention

Abdominal adipose tissue radiodensity is associated with survival after colorectal cancer

Background: Adipose tissue radiodensity may have prognostic importance for colorectal cancer (CRC) survival. Lower radiodensity is indicative of larger adipocytes, while higher radiodensity may represent adipocyte atrophy, inflammation, or edema. Objectives: We investigated associations of adipose tissue radiodensity and longitudinal changes in adipose tissue radiodensity with mortality among patients with nonmetastatic CRC. Methods: In 3023 patients with stage I-III CRC, radiodensities of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were quantified from diagnostic computed tomography (CT) images. There were 1775 patients with follow-up images available. Cox proportional hazards models and restricted cubic splines were used to examine associations of at-diagnosis values and of longitudinal changes in VAT and SAT radiodensities with risks of death after adjusting for potential confounders, including body size and comorbidities. Results: VAT and SAT radiodensities were linearly associated with all-cause mortality: The HRs for death per SD increase were 1.21 (95% CI, 1.11-1.32) for VAT radiodensity and 1.18 (95% CI, 1.11-1.26) for SAT radiodensity. Changes in adipose tissue radiodensity had curvilinear associations with risks of death. The HR for an increase in VAT radiodensity of at least 1 SD was 1.53 (95% CI, 1.23-1.90), while the HR for a decrease of at least 1 SD was nonsignificant at 1.11 (95% CI, 0.84-1.47) compared with maintaining radiodensity within 1 SD of baseline. Similarly, increases (HR, 1.88; 95% CI, 1.48-2.40) but not decreases (HR, 1.20; 95% CI, 0.94-1.54) in SAT radiodensity significantly increased the risk of death compared with no change in radiodensity. Conclusions: In patients with nonmetastatic CRC, adipose tissue radiodensity is a novel risk factor for total mortality that is independent of BMI and changes in body weight

Metabolic abnormalities and survival among patients with non-metastatic breast cancer

Abstract Background Research on the impact of metabolic abnormalities on breast cancer prognosis is limited by small samples and assessment of laboratory values at a single time point, often prior to cancer diagnosis and treatment. In this population-based cohort, time-updated laboratory values were adjusted for cancer treatment to assess the association between metabolic risk factors (glucose, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides) and breast cancer survival. Methods 13,434 women diagnosed with stage I-III breast cancer from 2005-15 at Kaiser Permanente were included. All outpatient fasting glucose, HDL-C, LDL-C, and triglyceride values from diagnosis through 2019 or death were extracted from electronic medical records. Risk of breast cancer-specific mortality was evaluated with Cox proportional hazards models adjusted for metabolic labs, demographics, body mass index, diabetes, dyslipidemia and anti-hypertensive medications, tumor characteristics (stage, ER and HER2 receptor status) and cancer treatment (use of chemotherapy, tamoxifen, and aromatase inhibitors). Results Mean (SD) age at diagnosis was 62.3 (11.8) years. Over a median follow-up of 8.6 years, 2,876 patients died; 1,080 of breast cancer. Patients with low HDL-C (≤ 45 vs. > 45 mg/dL) had higher breast cancer-specific mortality (HR, 1.77; 95% CI, 1.53-2.05), as did those with elevated fasting glucose (> 99 vs. 60-99 mg/dL) (HR, 1.19; 95% CI, 1.03-1.37). Elevated levels of triglycerides and LDL-C were not associated with breast cancer-specific mortality. Conclusions High fasting glucose and low HDL-C evaluated over time after cancer diagnosis were associated with higher breast cancer mortality independent of cancer treatments and changes in other metabolic risk factors. Future studies should address whether pharmacologic or lifestyle treatment of glucose and lipids after breast cancer diagnosis can optimize survival outcomes

Body composition from single versus multi‐slice abdominal computed tomography: Concordance and associations with colorectal cancer survival

Abstract Background Computed tomography (CT) scans are routinely obtained in oncology and provide measures of muscle and adipose tissue predictive of morbidity and mortality. Automated segmentation of CT has advanced past single slices to multi‐slice measurements, but the concordance of these approaches and their associations with mortality after cancer diagnosis have not been compared. Methods A total of 2871 patients with colorectal cancer diagnosed during 2012–2017 at Kaiser Permanente Northern California underwent abdominal CT scans as part of routine clinical care from which mid‐L3 cross‐sectional areas and multi‐slice T12–L5 volumes of skeletal muscle (SKM), subcutaneous adipose (SAT), visceral adipose (VAT) and intermuscular adipose (IMAT) tissues were assessed using Data Analysis Facilitation Suite, an automated multi‐slice segmentation platform. To facilitate comparison between single‐slice and multi‐slice measurements, sex‐specific z‐scores were calculated. Pearson correlation coefficients and Bland–Altman analysis were used to quantify agreement. Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for death adjusting for age, sex, race/ethnicity, height, and tumour site and stage. Results Single‐slice area and multi‐slice abdominal volumes were highly correlated for all tissues (SKM R = 0.92, P < 0.001; SAT R = 0.97, P < 0.001; VAT R = 0.98, P < 0.001; IMAT R = 0.89, P < 0.001). Bland–Altman plots had a bias of 0 (SE: 0.00), indicating high average agreement between measures. The limits of agreement were narrowest for VAT ( ± 0.42 SD) and SAT ( ± 0.44 SD), and widest for SKM ( ± 0.78 SD) and IMAT ( ± 0.92 SD). The HRs had overlapping CIs, and similar magnitudes and direction of effects; for example, a 1‐SD increase in SKM area was associated with an 18% decreased risk of death (HR = 0.82; 95% CI: 0.72–0.92), versus 15% for volume from T12 to L5 (HR = 0.85; 95% CI: 0.75–0.96). Conclusions Single‐slice L3 areas and multi‐slice T12–L5 abdominal volumes of SKM, VAT, SAT and IMAT are highly correlated. Associations between area and volume measures with all‐cause mortality were similar, suggesting that they are equivalent tools for population studies if body composition is assessed at a single timepoint. Future research should examine longitudinal changes in multi‐slice tissues to improve individual risk prediction