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

Change in Dietary Patterns and Change in Waist Circumference and DXA Trunk Fat Among Postmenopausal Women

OBJECTIVE: To examine whether changes in diet quality predict changes in central adiposity among postmenopausal women. METHODS: At baseline and 3-year follow-up, Women\u27s Health Initiative Observational Study participants completed food frequency questionnaires, and waist circumference was measured (WC, n = 67,175). In a subset, trunk fat was measured via dual-energy X-ray absorptiometry (DXA, n = 4,254). Using multivariable linear regression, 3-year changes in dietary patterns (Healthy Eating Index-2010, Alternate Healthy Eating Index-2010, Alternate Mediterranean Diet, and Dietary Approaches to Stop Hypertension) were examined as predictors of concurrent changes in WC and, secondarily, DXA. RESULTS: Mean (SD) age and 3-year changes in weight and WC were 63 (7) years, 0.52 (4.26) kg, and 0.94 (6.65) cm. A 10% increase in any dietary pattern score, representing improved diet quality, was associated with 0.07 to 0.43 cm smaller increase in WC over 3 years (all P \u3c 0.05). After adjusting for weight change, associations attenuated to 0.02 to 0.10 cm but remained statistically significant for all patterns except Alternate Mediterranean Diet. Results were similar for DXA trunk fat. CONCLUSIONS: Three-year improvements in diet quality are modestly protective against gain in WC and partially explained by lesser weight gain. Achieving and maintaining a healthful diet after menopause may protect against gains in central adiposity

Sleep Characteristics and Risk of Ovarian Cancer Among Postmenopausal Women

Several studies have assessed the relationship between sleep duration and ovarian cancer risk, but the results are conflicting. Importantly, no studies addressed the relationship between sleep disturbance or sleep quality and ovarian cancer incidence. Moreover, few studies have examined the relationships between sleep measures and subtypes of ovarian cancer. This study included 109,024 postmenopausal women ages 50-79 from the Women's Health Initiative during 1993-1998 and followed through 2018. The Cox proportional hazards model was used to estimate adjusted HRs for the associations between sleep habits and the incidence of ovarian cancer and its subtypes. No association was observed between sleep duration, sleep quality, sleep disturbance, or insomnia and risk of overall ovarian cancer, serous/nonserous, or type I/type II ovarian cancer subtype. However, compared with women with average sleep quality, women with restful or very restful sleep quality had a significantly lower risk of invasive serous subtype [HR: 0.73, 95% confidence interval (CI): 0.60-0.90] while insomnia was associated with a higher risk of invasive serous subtype (HR: 1.36, 95% CI: 1.12-1.66). Associations with insomnia differed significantly by serous and nonserous subtypes, and type I and type II subtypes ( = 0.001 and <0.001, respectively). This study provides no evidence on association between sleep habits and overall ovarian cancer risk among postmenopausal women. However, restful or very restful sleep quality was associated with a lower risk of invasive serous ovarian cancer, and insomnia was associated with a higher risk of invasive serous ovarian cancer. Associations with insomnia differed by subtypes. PREVENTION RELEVANCE: This study shows no association between sleep duration, sleep quality, or insomnia with the risk of overall ovarian cancer among postmenopausal women. However, restful sleep quality was associated with a lower risk of invasive serous ovarian cancer, and insomnia was associated with a higher risk of invasive serous ovarian cancer

Associations of pre‐existing co‐morbidities with skeletal muscle mass and radiodensity in patients with non‐metastatic colorectal cancer

Abstract Background and aim Co‐morbidities and computerized tomography‐measured muscle abnormalities are both common in cancer patients and independently adversely influence clinical outcomes. Muscle abnormalities are also evident in other diseases, such as diabetes and obesity. This study examined for the first time the association between co‐morbidities and muscle abnormalities in patients diagnosed with colorectal cancer (CRC). Methods This cross‐sectional study included 3051 non‐metastatic patients with Stages I–III CRC. Muscle abnormalities, measured at diagnosis, were defined as low skeletal muscle mass index (SMI) or low skeletal muscle radiodensity (SMD) quantified using computerized tomography images using optimal stratification. Co‐morbidities included in the Charlson index were ascertained. χ2 tests were used to compare the prevalence of co‐morbidities by the presence or absence of each muscle abnormality. Logistic regressions were performed to evaluate which co‐morbidities predicted muscle abnormalities adjusting for age, sex, body mass index, weight change, cancer stage, cancer site, race/ethnicity, and smoking. Results Mean age was 63 years; 50% of patients were male. The prevalence of low SMI and low SMD were 43.1% and 30.2%, respectively. Co‐morbidities examined were more prevalent in patients with low SMD than in those with normal SMD, and most remained independent predictors of low SMD after adjustment for covariates. Co‐morbidities associated with higher odds of low SMD included myocardial infarction [odds ratio (OR) = 1.77, P = 0.023], congestive heart failure (OR = 3.27, P < 0.001), peripheral vascular disease (OR = 2.15, P = 0.002), diabetes with or without complications (OR = 1.61, P = 0.008; OR = 1.46, P = 0.003, respectively), and renal disease (OR = 2.21, P < 0.001). By contrast, only diabetes with complications was associated with lower odds of low SMI (OR = 0.64, P = 0.007). Conclusions Prevalence of muscle abnormalities was high in patients with non‐metastatic CRC. Pre‐existing co‐morbidities were associated with low SMD, suggestive of a potential shared mechanism between fat infiltration into muscle and each of these co‐morbidities

Effect of resistance training on physical function during chemotherapy in colon cancer

Background: The decline of physical function during chemotherapy predicts poor quality of life and premature death. It is unknown if resistance training prevents physical function decline during chemotherapy in colon cancer survivors. Methods: This multicenter trial randomly assigned 181 colon cancer survivors receiving postoperative chemotherapy to home-based resistance training or usual care control. Physical function outcomes included the short physical performance battery, isometric handgrip strength, and the physical function subscale of the Medical Outcomes Short-Form 36-item questionnaire. Mixed models for repeated measures quantified estimated treatment differences. Results: At baseline, participants had a mean (SD) age of 55.2 (12.8) years; 67 (37%) were 60 years or older, and 29 (16%) had a composite short physical performance battery score of no more than 9. Compared with usual care control, resistance training did not improve the composite short physical performance battery score (estimated treatment difference = -0.01, 95% confidence interval [CI] = -0.32 to 0.31; P = .98) or the short physical performance battery scores for balance (estimated treatment difference = 0.01, 95% CI = -0.10 to 0.11; P = .93), gait speed (estimated treatment difference = 0.08, 95% CI = -0.06 to 0.22; P = .28), and sit-to-stand (estimated treatment difference = -0.08, 95% CI = -0.29 to 0.13; P = .46). Compared with usual care control, resistance training did not improve isometric handgrip strength (estimated treatment difference = 1.50 kg, 95% CI = -1.06 to 4.05; P = .25) or self-reported physical function (estimated treatment difference = -3.55, 95% CI = -10.03 to 2.94); P = .28). The baseline short physical performance battery balance score (r = 0.21, 95% CI = 0.07 to 0.35) and handgrip strength (r = 0.23, 95% CI = 0.09 to 0.36) correlated with chemotherapy relative dose intensity. Conclusion: Among colon cancer survivors with relatively high physical functioning, random assignment to home-based resistance training did not prevent physical function decline during chemotherapy

Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients

Abstract Background Body composition from computed tomography (CT) scans is associated with cancer outcomes including surgical complications, chemotoxicity, and survival. Most studies manually segment CT scans, but Automatic Body composition Analyser using Computed tomography image Segmentation (ABACS) software automatically segments muscle and adipose tissues to speed analysis. Here, we externally evaluate ABACS in an independent dataset. Methods Among patients with non‐metastatic colorectal (n = 3102) and breast (n = 2888) cancer diagnosed from 2005 to 2013 at Kaiser Permanente, expert raters annotated tissue areas at the third lumbar vertebra (L3). To compare ABACS segmentation results to manual analysis, we quantified the proportion of pixel‐level image overlap using Jaccard scores and agreement between methods using intra‐class correlation coefficients for continuous tissue areas. We examined performance overall and among subgroups defined by patient and imaging characteristics. To compare the strength of the mortality associations obtained from ABACS's segmentations to manual analysis, we computed Cox proportional hazards ratios (HRs) and 95% confidence intervals (95% CI) by tertile of tissue area. Results Mean ± SD age was 63 ± 11 years for colorectal cancer patients and 56 ± 12 for breast cancer patients. There was strong agreement between manual and automatic segmentations overall and within subgroups of age, sex, body mass index, and cancer stage: average Jaccard scores and intra‐class correlation coefficients exceeded 90% for all tissues. ABACS underestimated muscle and visceral and subcutaneous adipose tissue areas by 1–2% versus manual analysis: mean differences were small at −2.35, −1.97 and −2.38 cm2, respectively. ABACS's performance was lowest for the <2% of patients who were underweight or had anatomic abnormalities. ABACS and manual analysis produced similar associations with mortality; comparing the lowest to highest tertile of skeletal muscle from ABACS versus manual analysis, the HRs were 1.23 (95% CI: 1.00–1.52) versus 1.38 (95% CI: 1.11–1.70) for colorectal cancer patients and 1.30 (95% CI: 1.01–1.66) versus 1.29 (95% CI: 1.00–1.65) for breast cancer patients. Conclusions In the first study to externally evaluate a commercially available software to assess body composition, automated segmentation of muscle and adipose tissues using ABACS was similar to manual analysis and associated with mortality after non‐metastatic cancer. Automated methods will accelerate body composition research and, eventually, facilitate integration of body composition measures into clinical care