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Background: Epidemiological evidence suggests that inadequate sleep duration and insomnia may be associated with increased risk of metabolic syndrome (MetS). However, longitudinal data with repeated measures of sleep duration and insomnia and of MetS are limited. We examined the association of sleep duration and insomnia with MetS and its components using longitudinal data from the Women's Health Initiative (WHI). Methods: The study included postmenopausal women (ages 50–79 years) diabetes-free at enrollment in the WHI, with baseline data on sleep duration (n = 5,159), insomnia (n = 5,063), MetS, and its components. Repeated measures of self-reported sleep duration and insomnia were available from years 1 or 3 of follow-up and of the MetS components from years 3, 6 and 9. Associations were assessed using logistic regression and generalized estimating equations models, and odds ratios and 95% confidence intervals (CI) adjusted for major risk factors were calculated. Results: In cross-sectional analysis, baseline sleep duration ≥ 9 h was positively associated with MetS (OR = 1.51; 95%CI 1.12–2.04), while sleep duration of 8- < 9 h was associated with waist circumference > 88 cm and triglycerides ≥ 150 mg/dL (OR = 1.18; 95%CI 1.01–1.40 and OR = 1.23; 95%CI 1.05–1.46, respectively). Insomnia had a borderline positive association with MetS (OR = 1.14; 95%CI 0.99–1.31), and significant positive associations with waist circumference > 88 cm and glucose ≥ 100 mg/dL (OR = 1.18; 95%CI 1.03–1.34 and OR = 1.17; 95%CI 1.02–1.35, respectively). In the longitudinal analysis, change from restful sleep to insomnia over time was associated with increased odds of developing MetS (OR = 1.40; 95%CI 1.01–1.94), and of a triglyceride level ≥ 150 mg/dL (OR = 1.48; 95%CI 1.08–2.03). Conclusions: Among postmenopausal women in the WHI, sleep duration and insomnia were associated with current and future risk of MetS and some of its components. [ABSTRACT FROM AUTHOR]

Background: Many patients with colon cancer cannot fully adhere to postoperative chemotherapy due to dose-limiting toxicities, resulting in lower relative dose intensity (RDI) and potentially compromising overall survival. This study examined whether home-based resistance training (RT) during adjuvant chemotherapy improves RDI and patient-reported toxicities versus usual care (UC) in colon cancer patients. Methods: Multicenter, randomized control trial (RCT) conducted at community and academic practices. Enrollment of patients receiving postoperative chemotherapy for colon cancer occurred between February 23, 2018, and September 29, 2021; final follow-up was March 21, 2022. Participants were randomized to RT (n = 90) or UC (n = 91) for the duration of chemotherapy. Participants in the RT group engaged in twice weekly home-based progressive RT. At the end of the study, UC was given an online exercise program. Results: Among 181 randomized patients (mean age, 55.2 [SD, 12.8] years, 95 [52.5%] were men), there were no differences in the mean RDI among those in RT (79% [SD, 19%]) and those in UC (82% [SD, 19%]); (mean difference -0.04 [95% confidence interval (CI), -0.09 to 0.02]). Assignment to RT did not significantly reduce the number of moderate/severe symptoms per week across follow-up (relative rate: 0.94 [95% CI, 0.72-1.22]). Additionally, time since randomization did not significantly modify the effect of RT on the overall number of symptoms (p = .06). Conclusions: Among patients with colon cancer, these results do not support homebased RT as an adjunct to chemotherapy specifically to improve planned treatment intensity. [ABSTRACT FROM AUTHOR]

Background: There is emerging evidence that cancer and its treatments may accelerate the normal aging process, increasing the magnitude and rate of decline in functional capacity. This accelerated aging process is hypothesized to hasten the occurrence of common adverse age-related outcomes in cancer survivors, including loss of muscle mass and decrease in physical function. However, there is no data describing age-related loss of muscle mass and its relation to physical function in the long-term in cancer survivors. Methods: This study protocol describes the use of a novel method of muscle mass measurement, D3-creatine dilution method (D3Cr), in a large sample (n~6000) of community dwelling postmenopausal women from the Women's Health Initiative (WHI). D3Cr will be used to obtain a direct measure of muscle mass remotely. Participants will be drawn from two sub-cohorts embedded within the WHI that have recently completed an in-home visit. Cancer survivors will be drawn from the Life and Longevity After Cancer (LILAC) cohort, and cancer-free controls will be drawn from the WHI Long Life Study 2. The overall objective of this study is to examine the antecedents and consequences of low muscle mass in cancer survivors. The study aims are to: 1) create age-standardized muscle mass percentile curves and z-scores to characterize the distribution of D3- muscle mass in cancer survivors and non-cancer controls, 2) compare muscle mass, physical function, and functional decline in cancer survivors and non- cancer controls, and 3) use machine learning approaches to generate multivariate risk-prediction algorithms to detect low muscle mass. Discussion: The D3Cr method will transform our ability to measure muscle mass in large-scale epidemiologic research. This study is an opportunity to advance our understanding of a key source of morbidity among older and long-term female cancer survivors. This project will fill knowledge gaps, including the antecedents and consequences of low muscle mass, and use innovative methods to overcome common sources of bias in cancer research. The results of this study will be used to develop interventions to mitigate the harmful effects of low muscle mass in older adults and promote healthy survivorship in cancer survivors in the old (>65) and oldest-old (>85) age groups. [ABSTRACT FROM AUTHOR]

BACKGROUND: Physical activity is frequently proposed as an intervention to reduce fatigue and sleep disturbance in cancer survivors; however, the long-term effects of physical activity are often not reported, and older adults are typically excluded from these intervention studies. OBJECTIVES: This article aimed to examine if postdiagnosis physical activity is associated with lower long-term fatigue and sleep disturbance in older adult breast cancer survivors. METHODS: Data were analyzed of a prospective cohort of 440 breast cancer survivors aged 65 years or older from the Women's Health Initiative study. Multiple linear and logistic regression models were used to examine associations of physical activity with fatigue and sleep disturbance. FINDINGS: Higher postdiagnosis physical activity was associated with lower long-term fatigue but was not associated with lower sleep disturbance after adjusting for demographics, cancer characteristics, and baseline measures. [ABSTRACT FROM AUTHOR]

Background: The quantity and distribution of adipose tissue may be prognostic measures of mortality in colorectal cancer patients, and such associations may vary by patient sex.Methods: This cohort included 3262 stage I-III colorectal cancer patients. Visceral and subcutaneous adipose tissues were quantified using computed tomography. The primary endpoint was all-cause mortality. Restricted cubic splines estimated statistical associations with two-sided P values.Results: Visceral adipose tissue was prognostic of mortality in a reverse L-shaped pattern (nonlinear P = .02); risk was flat to a threshold (∼260 cm2) then increased linearly. Subcutaneous adipose tissue was prognostic of mortality in a J-shaped pattern (nonlinear P < .001); risk was higher at extreme (<50 cm2) but lower at intermediate values (>50 to ≤560 cm2). Patient sex modified the prognostic associations between visceral adipose tissue (Pinteraction = .049) and subcutaneous adipose tissue (Pinteraction = .04) with mortality. Among men, visceral adiposity was associated with mortality in a J-shaped pattern (nonlinear P = .003), whereas among women, visceral adiposity was associated with mortality in a linear pattern (linear P = .008). Among men, subcutaneous adiposity was associated with mortality in an L-shaped pattern (nonlinear P = .01), whereas among women, subcutaneous adiposity was associated with mortality in a J-shaped pattern (nonlinear P < .001).Conclusions: Visceral and subcutaneous adipose tissue were prognostic of mortality in patients with colorectal cancer; the shape of these associations were often nonlinear and varied by patient sex. These results offer insight into the potential biological mechanisms that link obesity with clinical outcomes in patients with cancer, suggesting that the dysregulated deposition of excess adiposity is prognostic of mortality. [ABSTRACT FROM AUTHOR]

Background: The empirical dietary inflammatory pattern (EDIP) score has been associated with concentrations of circulating inflammatory biomarkers in European Americans.Objective: We used the EDIP score, a weighted sum of 18 food groups that characterizes dietary inflammatory potential based on circulating concentrations of inflammatory biomarkers, to test the hypothesis that a pro-inflammatory dietary pattern is associated with inflammatory biomarker concentrations in a US multi-ethnic population.Methods: In this cross-sectional study, we calculated EDIP scores using baseline food frequency questionnaire data from 31,472 women, aged 50-79 y, in the Women's Health Initiative observational study and clinical trials. Circulating biomarkers outcomes at baseline were: C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor (TNF)-α, TNF receptor (TNFR) 1 and 2, and adiponectin. We used multivariable-adjusted linear regression analyses to estimate absolute concentrations and relative differences in biomarker concentrations, overall and in subgroups of race/ethnicity and BMI (body mass index) categories.Results: Independent of energy intake, BMI, physical activity, and other potential confounding variables, higher EDIP scores were significantly associated with higher (lower for adiponectin) absolute concentrations of all 6 biomarkers. On the relative scale, the percentage of difference in the concentration of biomarkers, among women in the highest compared to the lowest EDIP quintile, was: CRP, +13% (P-trend < 0.0001); IL-6, +15% (P-trend < 0.0001); TNF-α, +7% (P-trend = 0.0007); TNFR1, +4% (P-trend = 0.0009); TNFR2, +5% (P-trend < 0.0001); and adiponectin, -13% (P-trend <0.0001). These associations differed by racial/ethnic groups and by BMI categories. Whereas the absolute biomarker concentrations were lower among European-American women and among normal-weight women, the associations with diet were stronger than among women of African-American or Hispanic/Latino origin and among overweight and obese women.Conclusions: Findings demonstrate the successful replication of an empirical hypothesis-oriented a posteriori dietary pattern score in a multi-ethnic population of postmenopausal women, with subgroup differences by race/ethnicity and body weight. Future research needs to apply the score in non-US populations. [ABSTRACT FROM AUTHOR]

Computed Tomography (CT) imaging is widely used for studying body composition, i.e., the proportion of muscle and fat tissues with applications in areas such as nutrition or chemotherapy dose design. In particular, axial CT slices from the 3rd lumbar (L3) vertebral location are commonly used for body composition analysis. However, selection of the third lumbar vertebral slice and the segmentation of muscle/fat in the slice is a tedious operation if performed manually. The objective of this study is to automatically find the middle axial slice at L3 level from a full or partial body CT scan volume and segment the skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and intermuscular adipose tissue (IMAT) on that slice. The proposed algorithm includes an L3 axial slice localization network followed by a muscle-fat segmentation network. The localization network is a fully convolutional classifier trained on more than 12,000 images. The segmentation network is a convolutional neural network with an encoder–decoder architecture. Three datasets with CT images taken for patients with different types of cancers are used for training and validation of the networks. The mean slice error of 0.87 ± 2.54 was achieved for L3 slice localization on 1748 CT scan volumes. The performance of five class tissue segmentation network evaluated on two datasets with 1327 and 1202 test samples. The mean Jaccard score of 97% was achieved for SM and VAT tissue segmentation on 1327 images. The mean Jaccard scores of 98% and 83% are corresponding to SAT and IMAT tissue segmentation on the same dataset. The localization and segmentation network performance indicates the potential for fully automated body composition analysis with high accuracy. • The proposed algorithm automatically extracts and segments the L3 CT slice. • The L3 slice extraction model is a binary (L3 or non-L3) classification network. • The five-class segmentation network segments muscle and fat tissues on the L3 slice. • Mean slice error of 0.87 ± 2.54 achieved for L3 slice localization on 1748 CT scan. • The tissue segmentation network achieved high Jaccard scores on two test datasets. [ABSTRACT FROM AUTHOR]