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INTRODUCTION: Bumetanide, a loop diuretic, was identified as a candidate drug for repurposing for Alzheimers disease (AD) based on its effects on transcriptomic apolipoprotein E signatures. Cross-sectional analyses of electronic health records suggest that bumetanide is associated with decreased prevalence of AD; however, temporality between bumetanide exposure and AD development has not been established. METHODS: We evaluated Medicare claims data using Cox proportional hazards regression to evaluate the association between time-dependent use of bumetanide and time to first AD diagnosis while controlling for patient characteristics. Multiple sensitivity analyses were conducted to test the robustness of the findings. RESULTS: We sampled 833,561 Medicare beneficiaries, 60.8% female, with mean (standard deviation) age of 70.4 (12). Bumetanide use was not significantly associated with AD risk (hazard ratio 1.05; 95% confidence interval, 0.99-1.10). DISCUSSION: Using a nationwide dataset and a retrospective cohort study design, we were not able to identify a time-dependent effect of bumetanide lowering AD risk. HIGHLIGHTS: Bumetanide was identified as a candidate for repurposing for Alzheimers disease (AD). We evaluated the association between bumetanide use and risk of AD. We used Medicare data and accounted for duration of bumetanide use. Bumetanide use was not significantly associated with risk of AD.

Lactate elevation is a well-characterized biomarker of mitochondrial dysfunction, but its role in diabetic kidney disease (DKD) is not well defined. Urine lactate was measured in patients with type 2 diabetes (T2D) in 3 cohorts (HUNT3, SMART2D, CRIC). Urine and plasma lactate were measured during euglycemic and hyperglycemic clamps in participants with type 1 diabetes (T1D). Patients in the HUNT3 cohort with DKD had elevated urine lactate levels compared with age- and sex-matched controls. In patients in the SMART2D and CRIC cohorts, the third tertile of urine lactate/creatinine was associated with more rapid estimated glomerular filtration rate decline, relative to first tertile. Patients with T1D demonstrated a strong association between glucose and lactate in both plasma and urine. Glucose-stimulated lactate likely derives in part from proximal tubular cells, since lactate production was attenuated with sodium-glucose cotransporter-2 (SGLT2) inhibition in kidney sections and in SGLT2-deficient mice. Several glycolytic genes were elevated in human diabetic proximal tubules. Lactate levels above 2.5 mM potently inhibited mitochondrial oxidative phosphorylation in human proximal tubule (HK2) cells. We conclude that increased lactate production under diabetic conditions can contribute to mitochondrial dysfunction and become a feed-forward component to DKD pathogenesis.

BackgroundMany older adults spend the majority of their waking hours sitting, which increases their risk of chronic diseases. Given the challenges that many older adults face when engaging in moderate-to-vigorous physical activity, understanding the health benefits of decreasing sitting time and increasing the number of sit-to-stand transitions is needed to address this growing public health concern. ObjectiveThe aim of this 3-arm randomized controlled trial is to investigate how changes in sitting time and brief sit-to-stand transitions impact biomarkers of healthy aging and physical, emotional, and cognitive functioning compared with a healthy attention control arm. MethodsSedentary and postmenopausal women (N=405) will be recruited and randomly assigned to 1 of the 3 study conditions for 3 months: healthy living attention control (Healthy Living), reduce sitting time (Reduce Sitting), and increase sit-to-stand transitions (Increase Transitions). Assessments conducted at baseline and 3 months included fasting blood draw, blood pressure, anthropometric measurements, physical functioning, cognitive testing, and 7 days of a thigh-worn accelerometer (activPAL) and a hip-worn accelerometer (ActiGraph). Blood-based biomarkers of healthy aging included those associated with glycemic control (glycated hemoglobin, fasting plasma insulin and glucose, and homeostatic model assessment of insulin resistance). ResultsRecruitment began in May 2018. The intervention is ongoing, with data collection expected to continue through the end of 2022. ConclusionsThe Rise for Health study is designed to test whether 2 different approaches to interrupting sitting time can improve healthy aging in postmenopausal women. Results from this study may inform the development of sedentary behavior guidelines and interventions to reduce sitting time in older adults. Trial RegistrationClinicalTrials.gov NCT03473145; https://clinicaltrials.gov/ct2/show/NCT03473145 International Registered Report Identifier (IRRID)DERR1-10.2196/28684

BACKGROUND: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). METHODS: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. RESULTS: At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized β ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. CONCLUSION: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. TRIAL REGISTRATION: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.

BACKGROUND: Sedentary behavior is a recognized mortality risk factor. The novel and validated convolutional neural network hip accelerometer posture algorithm highly accurately classifies sitting and postural changes compared with accelerometer count cut points. We examined the prospective associations of convolutional neural network hip accelerometer posture-classified total sitting time and mean sitting bout duration with all-cause and cardiovascular disease (CVD) death. METHODS AND RESULTS: Women (n=5856; mean±SD age, 79±7 years; 33% Black women, 17% Hispanic or Latina women, 50% White women) in the Womens Health Initiative Objective Physical Activity and Cardiovascular Health (OPACH) Study wore the ActiGraph GT3X+ for ~7 days from May 2012 to April 2014 and were followed through February 19, 2022 for all-cause and CVD death. The convolutional neural network hip accelerometer posture algorithm classified total sitting time and mean sitting bout duration from GT3X+ output. Over follow-up (median, 8.4 years; range, 0.1-9.9), there were 1733 deaths (632 from CVD). Adjusted Cox regression hazard ratios (HRs) comparing women in the highest total sitting time quartile (>696 min/d) to those in the lowest (15 minutes) to the shortest (

[This corrects the article DOI: 10.3389/fnins.2022.815872.].

Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics in which important information of PA temporal/diurnal patterns is lost. In this paper we propose a novel functional data analysis approach based on Riemann manifolds for modeling PA and its longitudinal changes. We model smoothed minute-level PA of a day as one-dimensional Riemann manifolds and longitudinal changes in PA in different visits as deformations between manifolds. The variability in changes of PA among a cohort of subjects is characterized via variability in the deformation. Functional principal component analysis is further adopted to model the deformations, and PC scores are used as a proxy in modeling the relation between changes in PA and health outcomes and/or interventions. We conduct comprehensive analyses on data from two clinical trials: Reach for Health (RfH) and Metabolism, Exercise and Nutrition at UCSD (MENU), focusing on the effect of interventions on longitudinal changes in PA patterns and how different modes of changes in PA influence weight loss, respectively. The proposed approach reveals unique modes of changes, including overall enhanced PA, boosted morning PA, and shifts of active hours specific to each study cohort. The results bring new insights into the study of longitudinal changes in PA and health and have the potential to facilitate designing of effective health interventions and guidelines.

BACKGROUND/OBJECTIVES: Sedentary behavior (SB) has both movement and postural components, but most SB research has only assessed low movement, especially in children. The purpose of this study was to compare estimates and health associations of SB when derived from a standard accelerometer cut-point, a novel sitting detection technique (CNN Hip Accelerometer Posture for Children; CHAP-Child), and both combined. METHODS: Data were from the International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE). Participants were 6103 children (mean ± SD age 10.4 ± 0.56 years) from 12 countries who wore an ActiGraph GT3X+ accelerometer on the right hip for approximately one week. We calculated SB time, mean SB bout duration, and SB breaks using a cut-point (SBmovement), CHAP-Child (SBposture), and both methods combined (SBcombined). Mixed effects regression was used to test associations of SB variables with pediatric obesity variables (waist circumference, body fat percentage, and body mass index z-score). RESULTS: After adjusting for MVPA, SBposture showed several significant obesity associations favoring lower mean SB bout duration (b = 0.251-0.449; all p

STUDY OBJECTIVES: Examining multiple dimensions of sleep health may better capture associations between sleep and health risks, including cardiometabolic disease (CMD). Hispanics have elevated risk for inadequate sleep and CMD biomarkers. Few studies have explored whether associations between sleep and CMD differ by Hispanic ethnicity. METHODS: Leveraging data from the Community of Mine (CoM) study, a cross-sectional investigation of 602 ethnically diverse participants, we derived accelerometer-measured sleep duration and efficiency, and self-reported sleep quality. Accelerometer-measured sleep exposures were analyzed both as continuous and categorical variables. Multivariate and quantile regression models were used to assess associations between sleep and CMD biomarkers (insulin resistance, systolic blood pressure, and low-density-lipoprotein cholesterol), controlling for age, sex, ethnicity, education, smoking status, and body mass index. We examined the potential effect modification of Hispanic ethnicity. RESULTS: We observed mixed results based on CMD biomarkers and sleep exposure. Increased sleep duration was significantly related to low-density lipoprotein cholesterol in adjusted models (estimate = 0.06; 95% CI: 0.02, 0.11). Poor sleep efficiency was associated with greater insulin resistance in the adjusted quantile (estimate = 0.20; 95% CI: 0.04, 0.36) model at the 90th percentile. Self-reported sleep quality was not associated with CMD outcomes. There was no evidence of effect modification by Hispanic ethnicity. CONCLUSIONS: In this cohort, sleep health measures were found to have mixed and at times opposing effects on CMD outcomes. These effects did not demonstrate an interaction with Hispanic ethnicity.

BackgroundThe growing amount of high dimensional biomolecular data has spawned new statistical and computational models for risk prediction and disease classification. Yet, many of these methods do not yield biologically interpretable models, despite offering high classification accuracy. An exception, the top-scoring pair (TSP) algorithm derives parameter-free, biologically interpretable single pair decision rules that are accurate and robust in disease classification. However, standard TSP methods do not accommodate covariates that could heavily influence feature selection for the top-scoring pair. Herein, we propose a covariate-adjusted TSP method, which uses residuals from a regression of features on the covariates for identifying top scoring pairs. We conduct simulations and a data application to investigate our method, and compare it to existing classifiers, LASSO and random forests.ResultsOur simulations found that features that were highly correlated with clinical variables had high likelihood of being selected as top scoring pairs in the standard TSP setting. However, through residualization, our covariate-adjusted TSP was able to identify new top scoring pairs, that were largely uncorrelated with clinical variables. In the data application, using patients with diabetes (n = 977) selected for metabolomic profiling in the Chronic Renal Insufficiency Cohort (CRIC) study, the standard TSP algorithm identified (valine-betaine, dimethyl-arg) as the top-scoring metabolite pair for classifying diabetic kidney disease (DKD) severity, whereas the covariate-adjusted TSP method identified the pair (pipazethate, octaethylene glycol) as top-scoring. Valine-betaine and dimethyl-arg had, respectively, ≥ 0.4 absolute correlation with urine albumin and serum creatinine, known prognosticators of DKD. Thus without covariate-adjustment the top-scoring pair largely reflected known markers of disease severity, whereas covariate-adjusted TSP uncovered features liberated from confounding, and identified independent prognostic markers of DKD severity. Furthermore, TSP-based methods achieved competitive classification accuracy in DKD to LASSO and random forests, while providing more parsimonious models.ConclusionsWe extended TSP-based methods to account for covariates, via a simple, easy to implement residualizing process. Our covariate-adjusted TSP method identified metabolite features, uncorrelated from clinical covariates, that discriminate DKD severity stage based on the relative ordering between two features, and thus provide insights into future studies on the order reversals in early vs advanced disease states.

BACKGROUND: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. METHODS: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35-99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. RESULTS: Mean errors (activPAL - CHAP-Adult) and 95% limits of agreement were: sedentary time -10.5 (-63.0, 42.0) min/day, breaks in sedentary time 1.9 (-9.2, 12.9) breaks/day, mean bout duration -0.6 (-4.0, 2.7) min, usual bout duration -1.4 (-8.3, 5.4) min, alpha .00 (-.04, .04), and time in ≥30-min bouts -15.1 (-84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: -2.0% (4.0%), -4.7% (12.2%), 4.1% (11.6%), -4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearsons correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. CONCLUSIONS: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.

BackgroundOlder adults are the least active population in the U.S. Low-income communities have fewer physical activity (PA) resources, contributing to less PA and increased chronic disease risk. This study assessed the effect of the multilevel, peer-led, Peer Empowerment Program 4 Physical Activity (PEP4PA) on moderate-to-vigorous PA (MVPA) and health outcomes, over 2 years of follow up.MethodsIn a cluster-randomized controlled trial, 12 senior or community centers serving low-income older adults were assigned to a PA intervention (n = 6) or usual programming (n = 6) condition. PEP4PA included self-monitoring, health coaching, group walks, social support, and community advocacy to improve walking conditions. The primary outcome was daily minutes of MVPA (7-day accelerometer). Secondary outcomes included Perceived Quality of Life (PQoL), 6-Minute Walk Test (6-MWT), blood pressure (BP), and depressive symptoms at baseline, 6, 12, 18 and 24 months. Mixed effects regression models estimated the effects on outcomes between groups over time and included random effects for repeated measures and center clustering. Effect modification by sex and income status was assessed. We calculated the incremental cost per daily minute of MVPA gained in the intervention group relative to the control group to assess cost effectiveness.ResultsWe enrolled 476 older adults (50 + years). Participants were on average 71 years old, 76% female, 60% low income, and 38% identified as racial or ethnic minorities. Compared to the control group, intervention participants sustained roughly a 10 min/day increase in MVPA from baseline at all time points and increased mean PQoL scores from unsatisfied at baseline to satisfied at 12, 18 and 24 months. Males and higher-income groups had greater improvements in MVPA. No significant effects were observed for 6-MWT or depressive symptoms, and BP results were mixed. The incremental cost per minute MVPA gained per person was $0.25, $0.09, $0.06, and $0.05 at 6, 12, 18 and 24 months, respectively.ConclusionsPEP4PA achieved increases in MVPA and PQoL in low-income older adults, over 2 years of follow up. The peer-led, community-based intervention provides a sustainable and cost-effective model to improve health behaviors in underserved, aging populations.Trial registrationClinicalTrials.gov ( NCT02405325 ) March 20, 2015.

Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the data would lead to large changes in the chosen feature subset, then many selected features are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metrics (MSE or AUC) with proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications with continuous or binary outcomes. We conclude that Stability is a preferred feature selection criterion over model prediction metrics because it better quantifies the reproducibility of the feature selection method.

PurposeTo examine long-term cognitive effects of chemotherapy and identify predictors among women with breast cancer (WBC).Patients and methodsSixty-nine WBC scheduled to receive chemotherapy, and 64 matched-controls with no cancer, participated. Objective and subjective cognition, total sleep time, nap time, circadian activity rhythms (CAR), sleep quality, fatigue, and depression were measured pre-chemotherapy (Baseline), end of cycle 4 (Cycle-4), and one-year post-chemotherapy (1-Year).ResultsWBC showed no change in objective cognitive measures from Baseline to Cycle-4 but significantly improved from both time points to 1-Year. Matched-controls showed an increase in test performance at all time points. WBC had significantly higher self-reported cognitive dysfunction at Cycle-4 and 1-Year compared to baseline and compared to matched-controls. Worse neuropsychological functioning was predicted by less robust CARs (i.e., inconsistent 24 h pattern), worse sleep quality, longer naps, and worse cognitive complaints. Worse subjective cognition was predicted by lower sleep quality and higher fatigue and depressed mood.ConclusionObjective testing showed increases in performance scores from pre- and post-chemotherapy to one year later in WBC, but matched-controls showed an increase in test performance from baseline to Cycle-4 and from Cycle-4 to 1-Year, likely due to a practice effect. The fact that WBC showed no practice effects may reflect a form of learning deficit. Compared with the matched-controls, WBC reported significant worsened cognitive function. In WBC, worse objective and subjective cognitive functioning were predicted by worse sleep and sleep-related behaviors (naps and CAR). Interventions that target sleep, circadian rhythms, and fatigue may benefit cognitive function in WBC.

BackgroundHip-worn accelerometer cut-points have poor validity for assessing children's sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data.MethodsParticipants were 278, 8-11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration).ResultsAcross participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL.ConclusionsThere was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.

PurposeThe goal of this study was to examine whether daily increased morning light exposure would maintain or improve sleep and the circadian pattern of relatively more activity in the day and less during the night in women undergoing chemotherapy for breast cancer.Patients and methodsParticipants were 39 women with newly diagnosed breast cancer, randomized to either 30-mins of daily morning bright white light (BWL) or dim red light (DRL). Sleep/wake was measured objectively for 72-h with wrist actigraphy and subjectively with the Pittsburgh Sleep Quality Index (PSQI) prior to and during chemotherapy cycles 1 and 4. The study was registered with the National Institutes of Health ClinicalTrials.gov (Clinical Trials number: NCT00478257).ResultsResults from actigraphy suggested that compared to the DRL group, women in the BWL group had longer night-time sleep, fewer sleep disturbances during the night, and had fewer and shorter daytime naps at the end of cycle 4 of chemotherapy as well as exhibiting less activity at night and more activity during the day by the end of cycle 4. Results from PSQI indicated that components of sleep quality improved but daytime dysfunction deteriorated during cycle 4 treatment in the BWL group; meanwhile the DRL group used more sleep medications in the treatment weeks which might have led to the improved sleep quality during the recovery weeks of both cycles.ConclusionThese results suggest that bright white light therapy administered every morning on awakening may protect women undergoing chemotherapy for breast cancer from nighttime sleep and daytime wake disruption. Randomized clinical trials in larger samples are needed to confirm these findings.

IntroductionSitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method.MethodsCHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification).ResultsFor minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%-83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP's positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min).ConclusionCHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes.

Time spent sitting is positively correlated with endothelial dysfunction and cardiovascular disease risk. The underlying molecular mechanisms are unknown. MicroRNAs contained in extracellular vesicles (EVs) reflect cell/tissue status and mediate intercellular communication. We explored the association between sitting patterns and microRNAs isolated from endothelial cell (EC)-derived EVs. Using extant actigraphy based sitting behavior data on a cohort of 518 postmenopausal overweight/obese women, we grouped the woman as Interrupted Sitters (IS; N = 18) or Super Sitters (SS; N = 53) if they were in the shortest or longest sitting pattern quartile, respectively. The cargo microRNA in EC-EVs from the IS and SS women were compared. MicroRNA data were weighted by age, physical functioning, MVPA, device wear days, device wear time, waist circumference, and body mass index. Screening of CVD-related microRNAs demonstrated that miR-199a-5p, let-7d-5p, miR-140-5p, miR-142-3p, miR-133b level were significantly elevated in SS compared to IS groups. Group differences in let-7d-5p, miR-133b, and miR-142-3p were validated in expanded groups. Pathway enrichment analyses show that mucin-type O-glycan biosynthesis and cardiomyocyte adrenergic signaling (P

BackgroundMachine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior "in the wild." Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms.MethodTwenty-eight free-living women wore an ActiGraph GT3X+accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task.ResultsThe CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering.ConclusionUsing the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model's ability to deal with the complexity of free-living data and its potential transferability to new populations.

Valid and reliable measures are needed to better understand the relationship between physical activity and crime. This paper provides a comprehensive psychometric evaluation of measures developed in the Safe and Fit Environments (SAFE) Study to assess a crime-PA conceptual framework. In addition to assessing the basic psychometric properties of each measure (e.g., variable distributions [item/scale level], internal consistency reliability), this study formally examined the measurement validity and invariance of measures across four age groups using confirmatory factor analysis. The sample (n = 2173) included 336 Adolescents (aged 12-17), 532 Young adults (aged 18-39), 838 Middle Age Adults, and 467 Older Adults (aged 66+). The psychometric evaluation of (sub)scales showed consistent factorial validity and internal consistency reliability across the majority of the measures and across the four age groups. Specifically, 14 of the 17 measures displayed statistically and practically significant factor loadings and internal consistency values in the overall sample and across the age groups. The pattern of correlations for each (sub)scale with other (sub)scales/indexes largely did not exhibit redundancy across measures. The findings expanded upon the test-retest reliability evaluation reported in Patch et al. (2019), and clarified key aspects of the construct validity of these indicators. The latter bodes well for potential utility of these indicators in future predictive models.

This study employed novel GPS methods to assess the effect of a multilevel physical activity (PA) intervention on device-measured walking locations in 305 community dwelling older adults, ages 65+ (mean age = 83, 73% women). Retirement communities were randomized to a 1-year PA intervention that encouraged neighborhood walking, or to a healthy aging control condition. Total time and time spent walking in four life-space domains were assessed using GPS and accelerometer devices. The intervention increased the time spent walking as a proportion of total time spent in the Campus, Neighborhood and Beyond Neighborhood domains. Intervention effects on walking location were observed in both genders and across physical and cognitive functioning groups. Results demonstrate that an intervention providing individual, social and environmental support for walking can increase PA in larger life-space domains for a broad spectrum of older adults.

Active travel (AT) provides an opportunity to alleviate the physical inactivity and climate crises contributing to the global chronic disease burden, including cardiovascular diseases (CVD). Though AT shows promising links to reduced CVD risk, prior studies relied on self-reported AT assessment. In the present study, device-measured and self-reported AT were compared across population subgroups and relationships with CVD risk biomarkers were evaluated for both measures. The study recruited an ethnically diverse sample (N = 602, mean age 59 years, 42% Hispanic/Latino ethnicity) from neighborhoods that varied by walkability and food access. AT was assessed using concurrently collected accelerometer and GPS data and self-report data from a validated survey. Relationships with body mass index (BMI), triglycerides, high-density lipoprotein (HDL) cholesterol, blood pressure (BP), and moderate-to-vigorous physical activity (MVPA) were modeled using multivariable linear regression. Devices captured more AT than did self-report. We found differences in AT measures by population subgroups, including race, ethnicity, education, income, vehicle access, and walkability. Men had more accelerometer-measured MVPA, though women self-reported more daily minutes. Both device and survey AT measures were positively associated with total accelerometer-measured MVPA, though the relationship was stronger with device-measured AT. Device-measured AT was associated with lower BMI. No other CVD risk biomarker was associated with either AT measure. No effect modification by Hispanic/Latino ethnicity was detected. Further studies with device-based measures are warranted to better understand the relationship between AT and cardiovascular health.

IntroductionDifficulties with cognition are extremely common among breast cancer survivors and can significantly impact quality of life, daily functioning, and ability to return to work. One promising intervention is increasing physical activity, as it has been effective in improving cognition in non-cancer populations. Few physical activity intervention trials with cognition outcomes have included cancer survivors. This project builds upon our previous work indicating that increased physical activity can improve objectively measured processing speed and self-reported cognition among breast cancer survivors.MethodsThe I Can! study will examine whether a physical activity intervention improves cognition among 250 post-treatment breast cancer survivors (Stages I-III,

Study questionIs psychosocial stress associated with ovarian function in reproductive-aged survivors of cancer diagnosed as adolescents and young adults (AYA survivors)?Summary answerWe observed no association between self-reported and biomarkers of psychosocial stress and ovarian function in AYA survivors.What is known alreadyPsychosocial stress suppresses hypothalamic-pituitary-ovarian axis, resulting in ovulatory dysfunction, decreased sex steroidogenesis and lower fertility in reproductive-aged women. Many cancer survivors experience high psychosocial stress and hypothalamic-pituitary-adrenal axis dysregulation. The menstrual pattern disturbances and infertility they experience have been attributed to ovarian follicle destruction, but the contribution of psychosocial stress to these phenotypes is unknown.Study design, size, durationA cross-sectional study was conducted estimating the association between perceived stress, measured by self-report and saliva cortisol, and ovarian function, measured by bleeding pattern, dried blood spot (DBS) FSH and LH, and saliva estradiol. We included 377 AYA survivor participants.Participants/materials, setting, methodsAYA survivor participants were ages 15-35 at cancer diagnosis and ages 18-40 at study enrollment, had completed primary cancer treatment, had a uterus and at least one ovary, did not have uncontrolled endocrinopathy and were not on hormone therapy. Recruited from cancer registries, physician referrals and cancer advocacy groups, participants provided self-reported information on psychosocial stress (Perceived Stress Scale-10 (PSS-10)) and on cancer and reproductive (fertility, contraception, menstrual pattern) characteristics. DBS samples were collected timed to the early follicular phase (cycle Days 3-7) for menstruating individuals and on a random day for amenorrheic individuals; saliva samples were collected three time points within 1 day. FSH and LH were measured by DBS ELISAs, cortisol was measured by ELISA and estradiol was measured by liquid chromatography tandem mass spectrometry.Main results and the role of chanceThe median age of participants was 34.0 years (range 19-41) at a median of 6.0 years since cancer diagnosis. The most common cancer was breast (32.1%). Median PSS-10 score was 15 (range 0-36), with 5.3% scoring ≥26, the cut point suggestive of severe stress. Cortisol levels followed a diurnal pattern and cortisol AUC was negatively correlated with PSS-10 scores (P = 0.03). Neither PSS-10 scores nor cortisol AUC were associated with FSH, LH, estradiol levels or menstrual pattern. Waking and evening cortisol and the cortisol awakening response also were not related to ovarian function measures.Limitations, reasons for cautionOur analysis is limited by its cross-sectional nature, heterogeneity of cancer diagnosis and treatments and low prevalence of severe stress.Wider implications of the findingsThe lack of association between psychosocial stress and a variety of ovarian function measures in female AYA cancer survivors suggests that psychosocial stress does not have a significant impact on the reproductive axis of AYA survivors. This finding is important in counseling this population on their menstrual pattern and family building plans.Study funding/competing interest(s)NIH HD080952, South Korea Health Industry Development Institute HI18C1837 (JK). Dr A.D. works for Bluebird Bio, Inc., Dr D.Z. works for ZRT Labs and Dr P.M.S. works for Ansh Labs, which did not sponsor, support or have oversight of this research. Other authors report no competing interests.Trial registration numberN/A.

BackgroundSedentary behavior (SB) is linked to negative health outcomes in older adults. Most studies use summary values, e.g., total sedentary minutes/day. Diurnal timing of SB accumulation may further elucidate SB-health associations.MethodsSix thousand two hundred four US women (mean age = 79 ± 7; 50% White, 34% African-American) wore accelerometers for 7-days at baseline, yielding 41,356 person-days with > 600 min/day of data. Annual follow-up assessments of health, including physical functioning, were collected from participants for 6 years. A novel two-phase clustering procedure discriminated participants' diurnal SB patterns: phase I grouped day-level SB trajectories using longitudinal k-means; phase II determined diurnal SB patterns based on proportion of phase I trajectories using hierarchical clustering. Mixed models tested associations between SB patterns and longitudinal physical functioning, adjusted for covariates including total sedentary time. Effect modification by moderate-vigorous-physical activity (MVPA) was tested.ResultsFour diurnal SB patterns were identified: p1 = high-SB-throughout-the-day; p2 = moderate-SB-with-lower-morning-SB; p3 = moderate-SB-with-higher-morning-SB; p4 = low-SB-throughout-the-day. High MVPA mitigated physical functioning decline and correlated with better baseline and 6-year trajectory of physical functioning across patterns. In low MVPA, p2 had worse 6-year physical functioning decline compared to p1 and p4. In high MVPA, p2 had similar 6-year physical functioning decline compared to p1, p3, and p4.ConclusionsIn a large cohort of older women, diurnal SB patterns were associated with rates of physical functioning decline, independent of total sedentary time. In particular, we identified a specific diurnal SB subtype defined by less SB earlier and more SB later in the day, which had the steepest decline in physical functioning among participants with low baseline MVPA. Thus, diurnal timing of SB, complementary to total sedentary time and MVPA, may offer additional insights into associations between SB and physical health, and provide physicians with early warning of patients at high-risk of physical function decline.

Low concordance between studies that examine the role of microbiota in human diseases is a pervasive challenge that limits the capacity to identify causal relationships between host-associated microorganisms and pathology. The risk of obtaining false positives is exacerbated by wide interindividual heterogeneity in microbiota composition1, probably due to population-wide differences in human lifestyle and physiological variables2 that exert differential effects on the microbiota. Here we infer the greatest, generalized sources of heterogeneity in human gut microbiota profiles and also identify human lifestyle and physiological characteristics that, if not evenly matched between cases and controls, confound microbiota analyses to produce spurious microbial associations with human diseases. We identify alcohol consumption frequency and bowel movement quality as unexpectedly strong sources of gut microbiota variance that differ in distribution between healthy participants and participants with a disease and that can confound study designs. We demonstrate that for numerous prevalent, high-burden human diseases, matching cases and controls for confounding variables reduces observed differences in the microbiota and the incidence of spurious associations. On this basis, we present a list of host variables that we recommend should be captured in human microbiota studies for the purpose of matching comparison groups, which we anticipate will increase robustness and reproducibility in resolving the members of the gut microbiota that are truly associated with human disease.

Rationale & objectiveBiomarkers that provide reliable evidence of future diabetic kidney disease (DKD) are needed to improve disease management. In a cross-sectional study, we previously identified 13 urine metabolites that had levels reduced in DKD compared with healthy controls. We evaluated associations of these 13 metabolites with future DKD progression.Study designProspective cohort.Setting & participants1,001 Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes with estimated glomerular filtration rates (eGFRs) between 20 and 70mL/min/1.73m2 were followed up prospectively for a median of 8 (range, 2-10) years.Predictors13 urine metabolites, age, race, sex, smoked more than 100 cigarettes in lifetime, body mass index, hemoglobin A1c level, blood pressure, urinary albumin, and eGFR.OutcomesAnnual eGFR slope and time to incident kidney failure with replacement therapy (KFRT; ie, initiation of dialysis or receipt of transplant).Analytical approachSeveral clinical metabolite models were developed for eGFR slope as the outcome using stepwise selection and penalized regression, and further tested on the time-to-KFRT outcome. A best cross-validated (final) prognostic model was selected based on high prediction accuracy for eGFR slope and high concordance statistic for incident KFRT.ResultsDuring follow-up, mean eGFR slope was-1.83±1.92 (SD) mL/min/1.73m2 per year; 359 (36%) participants experienced KFRT. Median time to KFRT was 7.45 years from the time of entry to the CRIC Study. In our final model, after adjusting for clinical variables, levels of metabolites 3-hydroxyisobutyrate (3-HIBA) and 3-methylcrotonyglycine had a significant negative association with eGFR slope, whereas citric and aconitic acid were positively associated. Further, 3-HIBA and aconitic acid levels were associated with higher and lower risk for KFRT, respectively (HRs of 2.34 [95% CI, 1.51-3.62] and 0.70 [95% CI, 0.51-0.95]).LimitationsSubgroups for whom metabolite signatures may not be optimal, nontargeted metabolomics by flow-injection analysis, and 2-stage modeling approaches.ConclusionsUrine metabolites may offer insights into DKD progression. If replicated in future studies, aconitic acid and 3-HIBA could identify individuals with diabetes at high risk for GFR decline, potentially leading to improved clinical care and targeted therapies.

BackgroundEvidence suggests that short and long sleep durations are potential lifestyle factors associated with cardiovascular disease (CVD). Research on sleep duration and CVD risk is limited by use of self-report sleep measures, homogeneous populations, and studies on individual CVD risk factors. For women, risk of CVD and inadequate sleep duration increases with age. We hypothesized that accelerometer-measured sleep duration was associated with 10-year predicted probability of future CVD risk in a cohort of aging women.MethodsThis cross-sectional analysis included 3,367 older women (mean age 78.9 years; 53.3% White), from the Objective Physical Activity and Cardiovascular Health Study, ancillary study to the Women's Health Initiative. Women wore ActiGraph GT3X+ accelerometers on the hip for 24 hours/7 days. A 10-year predicted probability of future CVD risk, the Reynolds Risk Score (RRS), was computed using age, systolic blood pressure, high-sensitivity C-reactive protein (CRP), total and HDL cholesterol, diabetes mellitus status, smoking status, and family history of CVD. Average nightly sleep duration was derived from accelerometer data. Adjusted linear regression models investigated the association between sleep duration and RRS.ResultsResults suggested a U-shaped relationship between sleep duration and RRS, with both short and long sleep associated with higher RRS (p < .001). The association remained significant after adjustments for race/ethnicity, education, lifestyle factors, and health status indicators.ConclusionIn older women, actigraphy-ascertained sleep duration was associated with a 10-year predicted probability of future CVD risk. This study supports sleep duration as a modifiable risk factor for CVD in older women.

Background Sedentary behavior is pervasive, especially in older adults, and is associated with cardiometabolic disease and mortality. Relationships between cardiometabolic biomarkers and sitting time are unexplored in older women, as are possible ethnic differences. Methods and Results Ethnic differences in sitting behavior and associations with cardiometabolic risk were explored in overweight/obese postmenopausal women (n=518; mean±SD age 63±6 years; mean body mass index 31.4±4.8 kg/m2). Accelerometer data were processed using validated machine-learned algorithms to measure total daily sitting time and mean sitting bout duration (an indicator of sitting behavior pattern). Multivariable linear regression was used to compare sitting among Hispanic women (n=102) and non-Hispanic women (n=416) and tested associations with cardiometabolic risk biomarkers. Hispanic women sat, on average, 50.3 minutes less/day than non-Hispanic women (P

ImportanceGuidelines endorsing vegetable-enriched diets to improve outcomes for prostate cancer survivors are based on expert opinion, preclinical studies, and observational data.ObjectiveTo determine the effect of a behavioral intervention that increased vegetable intake on cancer progression in men with early-stage prostate cancer.Design, setting, and participantsThe Men's Eating and Living (MEAL) Study (CALGB 70807 [Alliance]) was a randomized clinical trial conducted at 91 US urology and medical oncology clinics that enrolled 478 men aged 50 to 80 years with biopsy-proven prostate adenocarcinoma (International Society of Urological Pathology grade group = 1 in those

BackgroundFor breast cancer survivors, moderate to vigorous physical activity (MVPA) is associated with improved survival. Less is known about the interrelationships of daytime activities (sedentary behavior [SB], light-intensity physical activity, and MVPA) and associations with survivors' health outcomes. This study will use isotemporal substitution to explore reallocations of time spent in daytime activities and associations with cancer recurrence biomarkers.MethodsBreast cancer survivors (N = 333; mean age 63 y) wore accelerometers and provided fasting blood samples. Linear regression models estimated the associations between daytime activities and cancer recurrence biomarkers. Isotemporal substitution models estimated cross-sectional associations with biomarkers when time was reallocated from of one activity to another. Models were adjusted for wear time, demographics, lifestyle factors, and medical conditions.ResultsMVPA was significantly associated with lower insulin, C-reactive protein, homeostatic model assessment of insulin resistance, and glucose, and higher sex hormone-binding globulin (all P < .05). Light-intensity physical activity and SB were associated with insulin and homeostatic model assessment of insulin resistance (both P < .05). Reallocating 18 minutes of SB to MVPA resulted in significant beneficial associations with insulin (-9.3%), homeostatic model assessment of insulin resistance (-10.8%), glucose (-1.7%), and sex hormone-binding globulin (7.7%). There were no significant associations when 79 minutes of SB were shifted to light-intensity physical activity.ConclusionsResults illuminate the possible benefits for breast cancer survivors of replacing time spent in SB with MVPA.

The incidence of type 2 diabetes is increasing more rapidly in adolescents than in any other age group. We identified and compared metabolite signatures in obese children with type 2 diabetes (T2D), obese children without diabetes (OB), and healthy, age- and gender-matched normal weight controls (NW) by measuring 273 analytes in fasting plasma and 24-hour urine samples from 90 subjects by targeted LC-MS/MS. Diabetic subjects were within 2 years of diagnosis in an attempt to capture early-stage disease prior to declining renal function. We found 22 urine metabolites that were uniquely associated with T2D when compared to OB and NW groups. The metabolites most significantly elevated in T2D youth included members of the betaine pathway, nucleic acid metabolism, and branched-chain amino acids (BCAAs) and their catabolites. Notably, the metabolite pattern in OB and T2D groups differed between urine and plasma, suggesting that urinary BCAAs and their intermediates behaved as a more specific biomarker for T2D, while plasma BCAAs associated with the obese, insulin resistant state independent of diabetes status. Correlative analysis of metabolites in the T2D signature indicated that betaine metabolites, BCAAs, and aromatic amino acids were associated with hyperglycemia, but BCAA acylglycine derivatives and nucleic acid metabolites were linked to insulin resistance. Of major interest, we found that urine levels of succinylaminoimidazole carboxamide riboside (SAICA-riboside) were increased in diabetic youth, identifying urine SAICA-riboside as a potential biomarker for T2D.

PurposeMulti-gene signatures provide biological insight and risk stratification in breast cancer. Intrinsic molecular subtypes defined by mRNA expression of 50 genes (PAM50) are prognostic in hormone-receptor positive postmenopausal breast cancer. Yet, for 25-40% in the PAM50 intermediate risk group, long-term risk remains uncertain. Our study aimed to (i) test the long-term prognostic value of the PAM50 signature in pre- and post-menopausal breast cancer; (ii) investigate if the PAM50 model could be improved by addition of other mRNAs implicated in oncogenesis.MethodsWe used archived FFPE samples from 1723 breast cancer survivors; high quality reads were obtained on 1253 samples. Transcript expression was quantified using a custom codeset with probes for > 100 targets. Cox models assessed gene signatures for breast cancer relapse and survival.ResultsOver 15 + years of follow-up, PAM50 subtypes were (P  60% higher hazard. Addition of a 13-gene hypoxia signature improved prognostication with > 40% higher hazard in the highest vs lowest hypoxia tertiles.ConclusionsPAM50 intrinsic subtypes were independently prognostic for long-term breast cancer survival, irrespective of menopausal status. Addition of hypoxia signatures improved risk prediction. If replicated, incorporating the 13-gene hypoxia signature into the existing PAM50 risk assessment tool, may refine risk stratification and further clarify treatment for breast cancer.

BackgroundPhysical inactivity and unhealthy diet are modifiable behaviors that lead to several cancers. Biologically, these behaviors are linked to cancer through obesity-related insulin resistance, inflammation, and oxidative stress. Individual strategies to change physical activity and diet are often short lived with limited effects. Interventions are expected to be more successful when guided by multi-level frameworks that include environmental components for supporting lifestyle changes. Understanding the role of environment in the pathways between behavior and cancer can help identify what environmental conditions are needed for individual behavioral change approaches to be successful, and better recognize how environments may be fueling underlying racial and ethnic cancer disparities.MethodsThis cross-sectional study was designed to select participants (n = 602 adults, 40% Hispanic, in San Diego County) from a range of neighborhoods ensuring environmental variability in walkability and food access. Biomarkers measuring cancer risk were measured with fasting blood draw including insulin resistance (fasting plasma insulin and glucose levels), systemic inflammation (levels of CRP), and oxidative stress measured from urine samples. Objective physical activity, sedentary behavior, and sleep were measured by participants wearing a GT3X+ ActiGraph on the hip and wrist. Objective measures of locations were obtained through participants wearing a Qstarz Global Positioning System (GPS) device on the waist. Dietary measures were based on a 24-h food recall collected on two days (weekday and weekend). Environmental exposure will be calculated using static measures around the home and work, and dynamic measures of mobility derived from GPS traces. Associations of environment with physical activity, obesity, diet, and biomarkers will be measured using generalized estimating equation models.DiscussionOur study is the largest study of objectively measured physical activity, dietary behaviors, environmental context/exposure, and cancer-related biomarkers in a Hispanic population. It is the first to perform high quality measures of physical activity, sedentary behavior, sleep, diet and locations in which these behaviors occur in relation to cancer-associated biomarkers including insulin resistance, inflammation, impaired lipid metabolism, and oxidative stress. Results will add to the evidence-base of how behaviors and the built environment interact to influence biomarkers that increase cancer risk.Trial registrationClinicalTrials.gov NCT02094170 , 03/21/2014.

Diabetic kidney disease (DKD) can lead to end-stage kidney disease (ESKD) and mortality; however, few mechanistic biomarkers are available for high-risk patients, especially those without macroalbuminuria. Urine from participants with diabetes from the Chronic Renal Insufficiency Cohort (CRIC) study, the Singapore Study of Macro-angiopathy and Micro-vascular Reactivity in Type 2 Diabetes (SMART2D), and the American Indian Study determined whether urine adenine/creatinine ratio (UAdCR) could be a mechanistic biomarker for ESKD. ESKD and mortality were associated with the highest UAdCR tertile in the CRIC study and SMART2D. ESKD was associated with the highest UAdCR tertile in patients without macroalbuminuria in the CRIC study, SMART2D, and the American Indian study. Empagliflozin lowered UAdCR in nonmacroalbuminuric participants. Spatial metabolomics localized adenine to kidney pathology, and single-cell transcriptomics identified ribonucleoprotein biogenesis as a top pathway in proximal tubules of patients without macroalbuminuria, implicating mTOR. Adenine stimulated matrix in tubular cells via mTOR and stimulated mTOR in mouse kidneys. A specific inhibitor of adenine production was found to reduce kidney hypertrophy and kidney injury in diabetic mice. We propose that endogenous adenine may be a causative factor in DKD., Introduction Progression to organ failure is marked by fibrosis and loss of architecture in solid organs, such as the kidney. In almost all progressive chronic kidney diseases (CKDs), the features [...]

BackgroundEmerging research suggests that increasing physical activity can help improve cognition among breast cancer survivors. However, little is known about the mechanism through which physical activity impacts cancer survivors' cognition.ObjectiveThe objective of this secondary analysis examined physical and psychological function potentially linking physical activity with changes in cognition among breast cancer survivors in a randomized controlled trial where the exercise arm had greater improvements in cognition than the control arm.MethodsA total of 87 sedentary breast cancer survivors were randomized to a 12-week physical activity intervention (n=43) or control condition (n=44). Objectively measured processing speed (National Institutes of Health Toolbox Oral Symbol Digit), self-reported cognition (patient-reported outcomes measurement information system [PROMIS] cognitive abilities), PROMIS measures of physical and psychological function (depression, anxiety, fatigue, and physical functioning), and plasma biomarkers (brain-derived neurotrophic factor, homeostatic model assessment 2 of insulin resistance, and C-reactive protein [CRP]) were collected at baseline and 12 weeks. Linear mixed-effects models tested intervention effects on changes in physical and psychological function variables and biomarkers. Bootstrapping was used to assess mediation. Exploratory analyses examined self-reported cognitive abilities and processing speed as mediators of the intervention effect on physical functioning.ResultsParticipants in the exercise arm had significantly greater improvements in physical functioning (beta=1.23; 95% CI 2.42 to 0.03; P=.049) and reductions in anxiety (beta=-1.50; 95% CI -0.07 to -2.94; P=.04) than those in the control arm. Anxiety significantly mediated the intervention effect on cognitive abilities (bootstrap 95% CI -1.96 to -0.06), whereas physical functioning did not (bootstrap 95% CI -1.12 to 0.10). Neither anxiety (bootstrap 95% CI -1.18 to 0.74) nor physical functioning (bootstrap 95% CI -2.34 to 0.15) mediated the intervention effect on processing speed. Of the biomarkers, only CRP had greater changes in the exercise arm than the control arm (beta=.253; 95% CI -0.04 to 0.57; P=.09), but CRP was not associated with cognition; therefore, none of the biomarker measures mediated the intervention effect on cognition. Neither cognitive abilities (bootstrap 95% CI -0.06 to 0.68) nor processing speed (bootstrap 95% CI -0.15 to 0.63) mediated the intervention effect on physical function.ConclusionsPhysical activity interventions may improve self-reported cognition by decreasing anxiety. If supported by larger studies, reducing anxiety may be an important target for improving self-reported cognition among cancer survivors.Trial registrationClinicalTrials.gov NCT02332876; https://clinicaltrials.gov/ct2/show/NCT02332876.