Predictors of attendance during an exercise program for cancer survivors.

PURPOSE: Exercise programs delivered in community- or clinic-based settings improve physical and psychosocial outcomes among cancer survivors; however, adherence is essential to achieve such benefits. This study examined predictors of attendance to an exercise program in a large, diverse sample of cancer survivors. METHODS: Participants (n = 302) were enrolled in BfitBwell, an exercise program for adults diagnosed with cancer, and currently receiving or within 6 months of completing chemotherapy or radiation therapy. Participants were offered two supervised aerobic and resistance exercise sessions per week for 3 months. Predictors of attendance included demographics, cancer-related information, quality of life (QOL), fatigue, physical fitness, activity level, and importance of making various changes (e.g., improving fitness). Univariate linear regression first explored associations between predictor variables and adherence, and any important variables (p < .10) were included in a multivariate linear regression model. RESULTS: Participants were M = 54.9 ± 13.9 years old, mostly female (67.3%), white (83.6%), and most commonly diagnosed with breast cancer (34.8%). Average attendance was 16.2 ± 6.6 exercise sessions. Six-minute walk test distance, QOL, and fatigue were associated with exercise session attendance (p < .05). The multivariable model revealed that higher QOL predicted higher attendance (ß = .351, p = .005), and working full- or part-time significantly predicted lower attendance (ß =- .221, p =.021). CONCLUSIONS: Higher pre-program QOL and not working full- or part-time predicted higher exercise program attendance. Existing and future exercise programs for cancer survivors should consider ways to adapt program delivery to provide support to survivors who start with low QOL, and accommodate those who may face barriers to attending due to work schedule/conflict.

The Microbiome, Epigenome, and Diet in Adults with Obesity during Behavioral Weight Loss.

Obesity has been linked to the gut microbiome, epigenome, and diet, yet these factors have not been studied together during obesity treatment. Our objective was to evaluate associations among gut microbiota (MB), DNA methylation (DNAme), and diet prior to and during a behavioral weight loss intervention. Adults (n = 47, age 40.9 ± 9.7 years, body mass index (BMI) 33.5 ± 4.5 kg/m2, 77% female) with data collected at baseline (BL) and 3 months (3 m) were included. Fecal MB was assessed via 16S sequencing and whole blood DNAme via the Infinium EPIC array. Food group and nutrient intakes and Healthy Eating Index (HEI) scores were calculated from 7-day diet records. Linear models were used to test for the effect of taxa relative abundance on DNAme and diet cross-sectionally at each time point, adjusting for confounders and a false discovery rate of 5%. Mean weight loss was 6.2 ± 3.9% at 3 m. At BL, one MB taxon, Ruminiclostridium, was associated with DNAme of the genes COL20A1 (r = 0.651, p = 0.029), COL18A1 (r = 0.578, p = 0.044), and NT5E (r = 0.365, p = 0.043). At 3 m, there were 14 unique MB:DNAme associations, such as Akkermansia with DNAme of GUSB (r = -0.585, p = 0.003), CRYL1 (r = -0.419, p = 0.007), C9 (r = -0.439, p = 0.019), and GMDS (r = -0.559, p = 0.046). Among taxa associated with DNAme, no significant relationships were seen with dietary intakes of relevant nutrients, food groups, or HEI scores. Our findings indicate that microbes linked to mucin degradation, short-chain fatty acid production, and body weight are associated with DNAme of phenotypically relevant genes. These relationships offer an initial understanding of the possible routes by which alterations in gut MB may influence metabolism during weight loss.

Development of a reference chart for monitoring cancer-related fatigue throughout a supervised exercise program.

BACKGROUND: Cancer-related fatigue (CRF) is one of the most reported and functionally limiting symptoms experienced by individuals living with and beyond cancer. Exercise is effective at reducing CRF, though currently it is not possible to predict the magnitude and time course of improvement for an individual participating in an exercise program. OBJECTIVE: To develop a reference chart of CRF improvement for individuals participating in a 3-month cancer-specific exercise program. METHODS: In this retrospective cohort study, CRF was assessed every two weeks (using the FACIT - Fatigue scale, range: 0 - 52 with lower scores indicating greater fatigue) in 173 individuals participating in a 3-month supervised exercise program (741 observations). No cancer types were excluded and individuals were either undergoing chemotherapy and/or radiation, or within 6 months of completing treatment. The reference chart was developed using Generalized Additive Models for Location Scale and Shape. RESULTS: Each participant had an average of four CRF observations. Lower centiles demonstrated greater improvement than higher centiles (11 points over the duration of the program for the 10th and 4 points for the 90th percentiles). LIMITATIONS: The population is biased to individuals self-selecting or being referred to a clinical exercise program. CONCLUSIONS: This reference chart provides a novel method of monitoring CRF improvement during a cancer-specific exercise program. Setting appropriate expectations and informing exercise prescription adaptation are discussed in the context of representative data from three participants. Future research can investigate improvements in clinical outcomes and the remote monitoring of CRF through the implementation of the reference chart.

Proteomics, dietary intake, and changes in cardiometabolic health within a behavioral weight-loss intervention: A pilot study.

OBJECTIVE: Identifying associations among circulating proteins, dietary intakes, and clinically relevant indicators of cardiometabolic health during weight loss may elucidate biologically relevant pathways affected by diet, allowing for an incorporation of precision nutrition approaches when designing future interventions. This study hypothesized that plasma proteins would be associated with diet and cardiometabolic health indicators within a behavioral weight-loss intervention. METHODS: This secondary data analysis included participants (n = 20, mean [SD], age: 40.1 [9.5] years, BMI: 34.2 [4.0] kg/m2 ) who completed a 1-year behavioral weight-loss intervention. Cardiovascular disease-related plasma proteins, diet, and cardiometabolic health indicators were evaluated at baseline and 3 months. Associations were determined via linear regression and integrated networks created using Visualization Of LineAr Regression Elements (VOLARE). RESULTS: A total of 16 plasma proteins were associated with ≥1 diet or health indicator at baseline (p < 0.001); changes in 42 proteins were associated with changes in diet or health indicators from baseline to 3 months (p < 0.005). Baseline tumor necrosis factor receptor superfamily member 10C (TNFRSF10C) was associated with intakes of dark green vegetables (r = -0.712), and fatty acid-binding protein 4 (FABP4) was associated with intakes of unsweetened coffee (r = -0.689). Changes in refined-grain intakes were associated with changes in scavenger receptor cysteine-rich type 1 protein M130 (CD163; r = 0.725), interleukin-1 receptor type 1 (IL1R-T1; r = 0.624), insulin (r = 0.656), and triglycerides (r = 0.648). CONCLUSIONS: Circulating cardiovascular disease-related proteins were associated with diet and cardiometabolic health indicators prior to and in response to weight loss.

Multiomic Predictors of Short-Term Weight Loss and Clinical Outcomes During a Behavioral-Based Weight Loss Intervention.

OBJECTIVE: Identifying predictors of weight loss and clinical outcomes may increase understanding of individual variability in weight loss response. We hypothesized that baseline multiomic features, including DNA methylation (DNAme), metabolomics, and gut microbiome, would be predictive of short-term changes in body weight and other clinical outcomes within a comprehensive weight loss intervention. METHODS: Healthy adults with overweight or obesity (n = 62, age 18-55 years, BMI 27-45 kg/m2 , 75.8% female) participated in a 1-year behavioral weight loss intervention. To identify baseline omic predictors of changes in clinical outcomes at 3 and 6 months, whole-blood DNAme, plasma metabolites, and gut microbial genera were analyzed. RESULTS: A network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A1c , homeostatic model assessment of insulin resistance, total cholesterol, triglycerides, C-reactive protein, leptin, and ghrelin) that changed significantly (P < 0.05). For eight of these, adjusted R2 ranged from 0.34 to 0.78. Our models identified specific DNAme sites, gut microbes, and metabolites that were predictive of variability in weight loss, waist circumference, and circulating triglycerides and that are biologically relevant to obesity and metabolic pathways. CONCLUSIONS: These data support the feasibility of using baseline multiomic features to provide insight for precision nutrition-based weight loss interventions.