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PDF - 94K, Detailed mixed models analysis of self-reported and biomarker data.

Epidemiological data suggest robust associations of high vegetable intake with decreased risks of bladder cancer incidence and mortality, but translational prevention studies have yet to be conducted. We designed and tested a novel intervention to increase vegetable intake in patients with noninvasive bladder cancer. We randomized 48 patients aged 50 to 80 years with biopsy-proven noninvasive (Ta, T1, or carcinoma in situ) urothelial cell carcinoma to telephone- and Skype-based dietary counseling or a control condition that provided print materials only. The intervention behavioral goals promoted seven daily vegetable servings, with at least two of these as cruciferous vegetables. Outcome variables were self-reported diet and plasma carotenoid and 24-hour urinary isothiocyanate (ITC) concentrations. We used two-sample t tests to assess between-group differences at 6-month follow-up. After 6 months, intervention patients had higher daily intakes of vegetable juice (P = 0.02), total vegetables (P = 0.02), and cruciferous vegetables (P = 0.07); lower daily intakes of energy (P = 0.007), fat (P = 0.002) and energy from fat (P = 0.06); and higher plasma α-carotene concentrations (P = 0.03). Self-reported cruciferous vegetable intake correlated with urinary ITC concentrations at baseline (P < 0.001) and at 6 months (P = 0.03). Although urinary ITC concentrations increased in the intervention group and decreased in the control group, these changes did not attain between-group significance (P = 0.32). In patients with noninvasive bladder cancer, our novel intervention induced diet changes associated with protective effects against bladder cancer. These data show the feasibility of implementing therapeutic dietary modifications to prevent recurrent and progressive bladder cancer. Cancer Prev Res; 6(9); 971–8. ©2013 AACR.

Supplementary Table 1 from Low to Moderate Alcohol Intake Is Not Associated with Increased Mortality after Breast Cancer

OBJECTIVE Understanding mechanisms underlying rapid estimated glomerular filtration rate (eGFR) decline is important to predict and treat kidney disease in type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS We performed a case-control study nested within four T1D cohorts to identify urinary proteins associated with rapid eGFR decline. Case and control subjects were categorized based on eGFR decline ≥3 and RESULTS The cohort study included 1,270 participants followed a median 8 years. In the discovery set, only cathepsin D peptide and protein were significant on full adjustment for clinical and laboratory variables. In the validation set, associations of cathepsin D with eGFR decline were replicated in minimally adjusted models but lost significance with adjustment for albuminuria. In a meta-analysis with combination of discovery and validation sets, the odds ratio for the association of cathepsin D with rapid eGFR decline was 1.29 per SD (95% CI 1.07–1.55). In complementary human cohorts, urine cathepsin D was associated with tubulointerstitial injury and tubulointerstitial cathepsin D expression was associated with increased cortical interstitial fractional volume. In mouse proximal tubular epithelial cell cultures, advanced glycation end product–BSA increased cathepsin D activity and inflammatory and tubular injury markers, which were further increased with cathepsin D siRNA. CONCLUSIONS Urine cathepsin D is associated with rapid eGFR decline in T1D and reflects kidney tubulointerstitial injury.

Introduction: Metabolomics could offer novel prognostic biomarkers and elucidate mechanisms of diabetic kidney disease (DKD) progression. Via metabolomic analysis of urine samples from 995 CRIC participants with diabetes and state-of-the-art statistical modeling, we aimed to identify metabolites prognostic to DKD progression. Methods: Urine samples (N = 995) were assayed for relative metabolite abundance by untargeted flow-injection mass spectrometry, and stringent statistical criteria were used to eliminate noisy compounds, resulting in 698 annotated metabolite ions. Utilizing the 698 metabolites' ion abundance along with clinical data (demographics, blood pressure, HbA1c, eGFR, and albuminuria), we developed univariate and multivariate models for the eGFR slope using penalized (lasso) and random forest models. Final models were tested on time-to-ESKD (end-stage kidney disease) via cross-validated C-statistics. We also conducted pathway enrichment analysis and a targeted analysis of a subset of metabolites. Results: Six eGFR slope models selected 9-30 variables. In the adjusted ESKD model with highest C-statistic, valine (or betaine) and 3-(4-methyl-3-pentenyl)thiophene were associated (p < 0.05) with 44% and 65% higher hazard of ESKD per doubling of metabolite abundance, respectively. Also, 13 (of 15) prognostic amino acids, including valine and betaine, were confirmed in the targeted analysis. Enrichment analysis revealed pathways implicated in kidney and cardiometabolic disease. Conclusions: Using the diverse CRIC sample, a high-throughput untargeted assay, followed by targeted analysis, and rigorous statistical analysis to reduce false discovery, we identified several novel metabolites implicated in DKD progression. If replicated in independent cohorts, our findings could inform risk stratification and treatment strategies for patients with DKD., American Journal of Nephrology, 53 (2-3), ISSN:0250-8095, ISSN:1421-9670

Introduction: Sedentary behavior (SB) is a recognized mortality and CVD risk factor. Most studies with accelerometry classified SB using cut-points, which do not capture postural transitions as accurately as thigh-worn devices. The recently published convolutional neural network hip accelerometer posture (CHAP) algorithm more accurately classifies sitting than cut-point methods. Hypothesis: Higher amounts of CHAP-classified sitting time (ST) and mean sitting bout duration (MSBD) are associated with higher all-cause (ACM) and CVD mortality risk. Methods: Older women (n=6,056; mean age=79±7; 34% Black, 17% Hispanic) in the Objective Physical Activity and Cardiovascular Health (OPACH) study without prior MI or stroke wore accelerometers for 7 days in May 2012-April 2014 and were followed through February 19, 2022 for mortality. The CHAP algorithm has been shown to have higher sensitivity (97.1% vs 88.2%) and specificity (88.6% vs 59.7%) for classifying sitting compared to the 100 counts/minute cut-point. Cox models estimated hazard ratios (HR) and 95% confidence intervals (CI) for ACM and CVD mortality adjusting for age, race/ethnicity, education, alcohol, smoking, multimorbidity, self-rated health, physical functioning, HDL, triglycerides, SBP, and log hs-CRP. Results: There were 1,808 deaths and 651 CVD deaths over a median follow-up of 8.4 years. The HR (95% CI; P-trend) comparing women in the highest ST quartile (>11.6 hr/day) to those in the lowest (15 minutes) to those in the lowest ( Conclusions: ST and MSBD were positively associated with ACM and CVD mortality risk, supporting interventions aimed at reducing both ST and MSBD.

Accelerometers are widely used for tracking human movement and provide minute-level (or even 30 Hz level) physical activity (PA) records for detailed analysis. Instead of using day-level summary statistics to assess these densely sampled inputs, we implement functional principal component analysis (FPCA) approaches to study the temporal patterns of PA data from 245 overweight/obese women at three visits over a 1-year period. We apply longitudinal FPCA to decompose PA inputs, incorporating subject-specific variability, and then test the association between these patterns and obesity-related health outcomes by multiple mixed effect regression models. With the proposed methods, the longitudinal patterns in both densely sampled inputs and scalar outcomes are investigated and connected. The results show that the health outcomes are strongly associated with PA variation, in both subject and visit-level. In addition, we reveal that timing of PA during the day can impact changes in outcomes, a finding that would not be possible with day-level PA summaries. Thus, our findings imply that the use of longitudinal FPCA can elucidate temporal patterns of multiple levels of PA inputs. Furthermore, the exploration of the relationship between PA patterns and health outcomes can be useful for establishing weight-loss guidelines.

Background Older 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. Methods In 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. Results We 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. Conclusions PEP4PA 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 registration ClinicalTrials.gov (NCT02405325) March 20, 2015.

Supplemental digital content is available in the text., Introduction Sitting 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. Methods CHAP 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). Results For 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). Conclusion CHAP 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.

Objective: Understanding mechanisms underlying rapid estimated glomerular filtration rate (eGFR) decline is important to predict and treat kidney disease in type 1 diabetes (T1D). Research Design and Methods: We performed a case-control study nested within four T1D cohorts to identify urinary proteins associated with rapid eGFR decline. Cases and controls were defined by eGFR decline >3 and 2/year, respectively. We used targeted liquid chromatography-tandem mass spectrometry to measure 38 peptides from 20 proteins implicated in diabetic kidney disease. Significant proteins were investigated in complementary human cohorts and in mouse proximal tubular epithelial cell cultures. Results: The cohort study included 1270 participants followed a median 8 years. In the discovery set, only cathepsin D peptide and protein were significant on full adjustment for clinical and laboratory variables. In the validation set, associations of cathepsin D with eGFR decline were replicated in minimally-adjusted models but lost significance with adjustment for albuminuria. In a meta-analysis combining discovery and validation sets, the odds ratio for the association of cathepsin D with rapid eGFR decline was 1.29 per SD (95%CI 1.07-1.55). In complementary human cohorts, urine cathepsin D was associated with tubulointerstitial injury, and tubulointerstitial cathepsin D expression was associated with increased cortical interstitial fractional volume. In mouse proximal tubular epithelial cell cultures, advanced glycation end product-bovine serum albumin increased cathepsin D activity and inflammatory and tubular injury markers, which were further increased with cathepsin D siRNA. Conclusion: Urine cathepsin D is associated with rapid eGFR decline in T1D and reflects kidney tubulointerstitial injury.

Purpose To examine long-term cognitive effects of chemotherapy and identify predictors among women with breast cancer (WBC). Patients and methods Sixty-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). Results WBC 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. Conclusion Objective 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.

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.

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.

Background: For 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. Methods: Breast 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. Results: MVPA 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 P Conclusions: Results illuminate the possible benefits for breast cancer survivors of replacing time spent in SB with MVPA.

Background Current physical activity guidelines focus on volume and intensity for CVD prevention rather than common behaviors responsible for movement, including those for daily living activities. We examined the associations of a machine‐learned, accelerometer‐measured behavior termed daily life movement (DLM) with incident CVD. Methods and Results Older women (n=5416; mean age, 79±7 years; 33% Black, 17% Hispanic) in the Women’s Health Initiative OPACH (Objective Physical Activity and Cardiovascular Health) study without prior CVD wore ActiGraph GT3X+ accelerometers for up to 7 days from May 2012 to April 2014 and were followed for physician‐adjudicated incident CVD through February 28th, 2020 (n=616 events). DLM was defined as standing and moving in a confined space such as performing housework or gardening. Cox models estimated hazard ratios (HR) and 95% CI, adjusting for age, race and ethnicity, education, alcohol use, smoking, multimorbidity, self‐rated health, and physical function. Restricted cubic splines examined the linearity of the DLM‐CVD dose‐response association. We examined effect modification by age, body mass index, Reynolds Risk Score, and race and ethnicity. Adjusted HR (95% CIs) across DLM quartiles were: 1.00 (reference), 0.68 (0.55–0.84), 0.70 (0.56–0.87), and 0.57 (0.45–0.74); p‐trend0.09). There was no evidence of effect modification by age, body mass index, Reynolds Risk Score, or race and ethnicity. Conclusions Higher DLM was independently associated with a lower risk of CVD in older women. Describing the beneficial associations of physical activity in terms of common behaviors could help older adults accumulate physical activity.

The human microbiome plays an important role in our health and identifying factors associated with microbiome composition provides insights into inherent disease mechanisms. By amplifying and sequencing the marker genes in high-throughput sequencing, with highly similar sequences binned together, we obtain Operational Taxonomic Units (OTU) profiles for each subject. Due to the high-dimensionality and non-normality features of the OTUs, the measure of diversity is introduced as a summarization at the microbial community level, including the distance-based Beta-diversity between individuals. Analyses of such between-subject attributes are not amenable to the predominant within-subject based statistical paradigm, such as t-tests and linear regression. In this paper, we propose a new approach to model Beta-diversity as a response within a regression setting by utilizing the functional response models (FRM), a class of semiparametric models for between- as well as within-subject attributes. The new approach not only addresses limitations of current methods for Beta-diversity with cross-sectional data, but also provides a premise for extending the approach to longitudinal and other clustered data in the future. The proposed approach is illustrated with both real and simulated data. This article is protected by copyright. All rights reserved.

ImportanceThere is controversy about the benefit of prostate-specific antigen (PSA) screening. Prostate-specific antigen screening rates have decreased since 2008 in the US, and the incidence of metastatic prostate cancer has increased. However, there is no direct epidemiologic evidence of a correlation between population PSA screening rates and subsequent metastatic prostate cancer rates.ObjectiveTo assess whether facility-level variation in PSA screening rates is associated with subsequent facility-level metastatic prostate cancer incidence.Design, Setting, and ParticipantsThis retrospective cohort used data for all men aged 40 years or older with an encounter at 128 facilities in the US Veterans Health Administration (VHA) from January 1, 2005, to December 31, 2019.ExposuresYearly facility-level PSA screening rates, defined as the proportion of men aged 40 years or older with a PSA test in each year, and long-term nonscreening rates, defined as the proportion of men aged 40 years or older without a PSA test in the prior 3 years, from January 1, 2005, to December 31, 2014.Main Outcomes and MeasuresThe main outcomes were facility-level yearly counts of incident metastatic prostate cancer diagnoses and age-adjusted yearly metastatic prostate cancer incidence rates (per 100 000 men) 5 years after each PSA screening exposure year.ResultsThe cohort included 4 678 412 men in 2005 and 5 371 701 men in 2019. Prostate-specific antigen screening rates decreased from 47.2% in 2005 to 37.0% in 2019, and metastatic prostate cancer incidence increased from 5.2 per 100 000 men in 2005 to 7.9 per 100 000 men in 2019. Higher facility-level PSA screening rates were associated with lower metastatic prostate cancer incidence 5 years later (incidence rate ratio [IRR], 0.91 per 10% increase in PSA screening rate; 95% CI, 0.87-0.96; P P = .01).Conclusions and RelevanceFrom 2005 to 2019, PSA screening rates decreased in the national VHA system. Facilities with higher PSA screening rates had lower subsequent rates of metastatic prostate cancer. These data may be used to inform shared decision-making about the potential benefits of PSA screening among men who wish to reduce their risk of metastatic prostate cancer.

Background Hip-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. Methods Participants 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). Results Across 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. Conclusions There 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.

Little is known about how sedentary behavior (SB) metrics derived from hip- and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL (AP) micro monitors were concurrently worn with hip-worn ActiGraph (AG) GT3X+ accelerometers (with SB measured using the 100 counts per minute [cpm] cut point; AG100cpm) by 953 older adults (age 77 ± 6.6, 54% women) for 4–7 days. Device agreement for sedentary time and five SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with four health outcomes using standardized (i.e., z scores) and unstandardized SB metrics. Mean errors (AP − AG100cpm) and 95% limits of agreement were: sedentary time −54.7 [−223.4, 113.9] min/day; time in 30+ min bouts 77.6 [−74.8, 230.1] min/day; mean bout duration 5.9 [0.5, 11.4] min; usual bout duration 15.2 [0.4, 30] min; breaks in sedentary time −35.4 [−63.1, −7.6] breaks/day; and alpha −.5 [−.6, −.4]. Respective Pearson correlations were: .66, .78, .73, .79, .51, and .40. Concordance correlations were: .57, .67, .40, .50, .14, and .02. The statistical significance and direction of associations were identical for AG100cpm and AP metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 13 of 24 tests for unstandardized and five of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from AG100cpm due to the tendency for it to overestimate breaks in sedentary time relative to AP. However, high correlations between AP and AG100cpm measures and similar standardized associations with health outcomes suggest that studies using AG100cpm are useful, though not ideal, for studying SB in older adults.

Acute kidney injury (AKI) is defined as a rapid decline in kidney function. The associated syndromes may lead to increased morbidity and mortality, but its early detection remains difficult. Using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS), we analyzed the urinary metabolomic profile of patients admitted to the intensive care unit (ICU) after invasive surgery. Urine samples were collected at six time points: before surgery, at ICU admission and 6, 12, 24 and 48 h after. First, urine samples from 61 initial patients (non-AKI: 23, mild AKI: 24, severe AKI: 14) were measured, followed by the measurement of urine samples from 60 additional patients (non-AKI: 40, mild AKI: 20). Glycine and ethanolamine were decreased in patients with AKI compared with non-AKI patients at 6–24 h in the two groups. The linear statistical model constructed at each time point by machine learning achieved the best performance at 24 h (median AUC, area under the curve: 89%, cross-validated) for the 1st group. When cross-validated between the two groups, the AUC showed the best value of 70% at 12 h. These results identified metabolites and time points that show patterns specific to subjects who develop AKI, paving the way for the development of better biomarkers.

Background: Machine 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. Method: Twenty-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. Results: The 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. Conclusion: Using 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.

OBJECTIVES Patients with type 1 diabetes (T1D) exhibit modest lipid abnormalities as measured by traditional metrics. This study aimed to identify lipidomic predictors of rapid decline of kidney function in T1D. RESEARCH DESIGN AND METHODS In a case-control study, 817 patients with T1D from three large cohorts were randomly split into training and validation subsets. Case was defined as >3 mL/min/1.73 m2 per year decline in estimated glomerular filtration rate (eGFR), while control was defined as RESULTS At individual lipids, free fatty acid (FFA)20:2 was directly and phosphatidylcholine (PC)16:0/22:6 was inversely and independently associated with rapid eGFR decline. When examined by lipid class, rapid eGFR decline was characterized by higher abundance of unsaturated FFAs, phosphatidylethanolamine (PE)-Ps, and PCs with an unsaturated acyl chain at the sn1 carbon, and by lower abundance of saturated FFAs, longer triacylglycerols, and PCs, PEs, PE-Ps, and PE-Os with an unsaturated acyl chain at the sn1 carbon at eGFR ≥90 mL/min/1.73 m2. A multilipid panel consisting of unsaturated FFAs and saturated PE-Ps predicted rapid eGFR decline better than individual lipids (C-statistic, 0.71) and improved the C-statistic of the clinical model from 0.816 to 0.841 (P = 0.039). Observations were confirmed in the validation subset. CONCLUSIONS Distinct from previously reported predictors of GFR decline in type 2 diabetes, these findings suggest differential incorporation of FFAs at the sn1 carbon of the phospholipids’ glycerol backbone as an independent predictor of rapid GFR decline in T1D.

Highlights • Newly-developed measures of crime, environmental context, and activity are valid. • Factorial validity of measures supported across four across age groups. • Minimal measurement redundancy found across measures., 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.

Author(s): Hartman, Sheri J; Nelson, Sandahl H; Marinac, Catherine R; Natarajan, Loki; Parker, Barbara A; Patterson, Ruth E | Abstract: ObjectiveBreast cancer survivors experience problems with cognition that interfere with daily life and can last for years. In the general population, obesity and diabetes are risk factors for cognitive decline, and weight loss can improve cognition; however, the impact of intentional weight loss on cancer survivors' cognition has not been tested. We investigated the impact of weight loss and metformin on changes in cognitive function in a sample of breast cancer survivors.MethodsOverweight/obese postmenopausal breast cancer survivors (nn=n333) were randomized to a weight loss intervention versus control and metformin versus placebo in a 2n×n2 factorial design. Outcomes were changes in five cognitive domains from baseline to 6nmonths measured by objective neurocognitive tests.ResultsThere were no statistically significant intervention effects for the metformin or weight loss interventions in five neurocognitive domains. Baseline body mass index (BMI) was a significant effect modifier of the changes in verbal functioning for the weight loss (Pn=n0.009) and metformin interventions (Pn=n0.0125). These effect modifications were independent of percent weight loss achieved during the 6-month study period.ConclusionsThis randomized controlled trial of weight loss and metformin interventions that examined changes to cognition among breast cancer survivors suggests that these interventions may not improve cognitive functioning among breast cancer survivors in general. However, weight loss may improve verbal functioning among individuals with a higher BMI.

This study compared five different methods for analyzing accelerometer-measured physical activity (PA) in older adults and assessed the relationship between changes in PA and changes in physical function and depressive symptoms for each method. Older adult females (N=144, M(age)=83.3±6.4yrs) wore hip accelerometers for six days and completed measures of physical function and depressive symptoms at baseline and six months. Accelerometry data were processed by five methods to estimate PA: 1041 vertical axis cut-point, 15-second vector magnitude (VM) cut-point, 1-second VM algorithm (Activity Index (AI)), machine learned walking algorithm, and individualized cut-point derived from a 400-meter walk. Generalized estimating equations compared PA minutes across methods and showed significant differences between some methods but not others; methods estimated 6-month changes in PA ranging from 4 minutes to over 20 minutes. Linear mixed models for each method tested associations between changes in PA and health. All methods, except the individualized cut-point, had a significant relationship between change in PA and improved physical function and depressive symptoms. This study is among the first to compare accelerometry processing methods and their relationship to health. It is important to recognize the differences in PA estimates and relationship to health outcomes based on data processing method.

Accelerometers are person-worn sensors that provide objective measurements of movement based on minute-level activity counts, thus providing a rich framework for assessing physical activity patterns. New statistical approaches and computational tools are needed to exploit these densely sampled time-series data. We implement a functional principal component mixed model approach to ascertain temporal activity patterns in 578 overweight women (60% cancer survivors) and summarize individual patterns with unique personalized principal component scores. We then test if these patterns are associated with health by performing multiple regression of health outcomes (including biomarkers, namely, insulin, C-reactive protein, and quality of life) on activity patterns represented by these scores. Our model elucidates the most important patterns/modes of variation in physical activities. Results show that health outcomes including biomarkers and quality of life are strongly associated with the total volume, as well as temporal variation in activity. In addition, associations between physical activity and health outcomes are not modified by cancer status. Our findings suggest that employing a multilevel functional principal component analysis approach can elicit important temporal patterns in physical activity. It further allows us to study the relationship between health outcomes and activity patterns, and thus could be a valuable modeling approach in behavioral research.

Physical 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. This 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. Our 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. ClinicalTrials.gov NCT02094170 , 03/21/2014.

Background There has been a rapid increase in the use of commercially available activity trackers, such as Fitbit, in physical activity intervention research. However, little is known about the long-term sustained use of trackers and behavior change after short-term interventions. Objective This study aims to use minute-level data collected from a Fitbit tracker for up to 2 years after the end of a randomized controlled trial to examine patterns of Fitbit use and activity over time. Methods Participants in this secondary data analysis were 75 female breast cancer survivors who had been enrolled in a 12-week physical activity randomized controlled trial. Participants randomized to the exercise intervention (full intervention arm) received a Fitbit One, which was worn daily throughout the 12-week intervention, and then were followed for 2 years after the intervention. Participants randomized to the waitlist arm, after completing the randomized controlled trial, received a Fitbit One and a minimal version of the exercise intervention (light intervention arm), and then were followed for 2 years after the intervention. Average and daily adherence and MVPA were compared between the 2 groups in the interventional and postinterventional periods using both linear and generalized additive mixed effects models. Results Adherence to wearing the Fitbit during the 12-week intervention period was significantly higher in the full intervention arm than in the light intervention arm (85% vs 60%; P Conclusions While adherence to wearing activity trackers and maintaining physical activities declined after completion of a 12-week exercise intervention, a more active interventional strategy resulted in greater wear time and activity levels during the intervention and more stable patterns of adherence and activity in the long term. An improved understanding of long-term maintenance patterns may inform improved exercise interventions that result in sustained increases in physical activity. Trial Registration ClinicalTrials.gov NCT02332876; https://clinicaltrials.gov/ct2/show/NCT02332876

Objective: Older adults are the least active population group in the U.S. Low income and ethnically diverse communities have fewer physical activity (PA) related resources and facilities, which contributes to lower PA levels and disparities in cardiovascular (CVD) risk factors and disease outcomes. This study assessed the hypothesis that low income and diverse older adults participating in the multilevel Peer Empowerment Program 4 Physical Activity (PEP4PA) would increase moderate-to-vigorous PA (MVPA), improve blood pressure (BP), perceived quality of life (PQoL), and depressive symptoms to a greater extent than older adults receiving usual senior center programming. Methods: In a cluster-randomized controlled trial (RCT) in 12 senior centers, 476 older adults (mean age 71.4 years, 76% female, 60% low income, 38% minority) were assigned to a PA intervention (n=267) or control (n=209) condition. The peer-led intervention included individual self-monitoring and counseling, group walks and social support, and community advocacy to improve walking conditions. Outcomes included minutes of MVPA per day (7-day accelerometer), systolic and diastolic BP (automatic cuff), PQoL (PQoL-20) and depressive symptoms (CES-D 10) at baseline, 6, 12, 18 and 24 months. To account for multiple measurement days and clustering of participants within senior centers, mixed effects regression models with random effects estimated the intervention effects on all outcomes between groups over time. Models were adjusted for imbalanced baseline covariates. A three-way interaction term assessed whether intervention effects differed by income status. Results: Compared to the control group, intervention participants significantly increased MVPA from baseline at 6, 12, 18 and 24 months by 8, 11, 9 and 9 minutes/day respectively (p Conclusions: The multilevel RCT achieved sustained increases in MVPA and QoL in a diverse cohort of older adults across 2 years of follow up. The peer-led, community-based intervention provides a sustainable model to improve health behaviors related to CVD in an at-risk and often difficult to reach aging population. Further exploration is needed to understand what components of the intervention may be modified to address the differential effects by income status.

Background: Targeting daily increases in physical activity (PA) is a common lifestyle strategy for cardiovascular disease (CVD) risk reduction. While less targeted in lifestyle interventions, poor sleep is also consistently linked to CVD. Emerging evidence connects changes in daily PA to beneficial changes in sleep; however, little is known about the impact of community-based PA interventions on habitual sleep among older adults. We leveraged accelerometer data from the Peer Empowerment Program for Physical Activity in Low Income & Minority Seniors (PEP4PA) randomized controlled trial to examine the effect of a PA intervention on accelerometer-measured sleep characteristics among older adults in low income and diverse senior center communities. Methods: PEP4PA participants (N=476; mean age=71.0 ± 9 years, 75% female, 60% low income, 36% non-white) were recruited from senior centers and randomized to a healthy control condition(n=209) or a peer-lead walking intervention (N=267). The peer-led PA intervention included self-monitoring techniques, health coach counseling, group walks, and community advocacy to improve walking conditions. Participants wore Actigraph GT3X+ accelerometers on the wrist at baseline, 6, 12, and 24 months. Data were processed for sleep characteristics including weekly average sleep duration and sleep efficiency. Mixed effects regression models provided estimates of the intervention effect on sleep characteristics at each time point. Models were adjusted for age, sex, and race. Three way interaction terms (time*treatment*sleep duration status) were used to assess differential intervention effects by short (8 hours) sleep duration status at baseline. Results: At baseline, the average sleep duration of participants was 7 hours (SD: 1.07 hours) and the average sleep efficiency was 87.0% (SD: 6.2%). Compared to participants in the control group, participants in the intervention group had shorter sleep durations (7.1 hours vs 7.0 hours; p value: 0.08) and poorer sleep efficiency 86.0% vs 88.2%; p Conclusions: Over the 2-year period, the PA intervention did not appear to have an impact on participants’ average sleep duration or quality. More investigation is needed into the temporal and daily relations of PA and sleep among older adults in interventions to understand if increases in daily PA are associated with better sleep characteristics at night.

To examine long-term cognitive effects of chemotherapy and identify predictors among women with breast cancer (WBC).Sixty-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).WBC 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.Objective 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.

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.

BACKGROUND Many 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. OBJECTIVE The 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. METHODS Sedentary 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). RESULTS Recruitment began in May 2018. The intervention is ongoing, with data collection expected to continue through the end of 2022. CONCLUSIONS The 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. CLINICALTRIAL ClinicalTrials.gov NCT03473145; https://clinicaltrials.gov/ct2/show/NCT03473145 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/28684

STUDY QUESTION Is psychosocial stress associated with ovarian function in reproductive-aged survivors of cancer diagnosed as adolescents and young adults (AYA survivors)? SUMMARY ANSWER We observed no association between self-reported and biomarkers of psychosocial stress and ovarian function in AYA survivors. WHAT IS KNOWN ALREADY Psychosocial 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, DURATION A 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, METHODS AYA 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 CHANCE The 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 CAUTION Our analysis is limited by its cross-sectional nature, heterogeneity of cancer diagnosis and treatments and low prevalence of severe stress. WIDER IMPLICATIONS OF THE FINDINGS The 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 NUMBER N/A

Background: The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups. Methods: Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health. Results: The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while Conclusion: The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5–2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.

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

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 selectionmethod.

Introduction: Increased sedentary behavior is independently associated with increased risk of cardiometabolic diseases. There are few community-based RCTs designed to address this behavior, and none among Latinas, who are particularly at risk for cardiometabolic diseases. This study presents the results of an intervention to reduce sitting time among postmenopausal overweight Latina women. Hypothesis: Participants randomized to the standing intervention arm will have a greater reduction in sitting time compared to those in the comparison arm. Methods: A total of 254 overweight, sedentary postmenopausal Latinas in San Diego were enrolled in a 12-week trial, and randomized to either an intervention to replace sitting time with standing bouts or a comparison group that received heart healthy information. The intervention, based on social-cognitive behavior change, included culturally-tailored coaching. Intervention participants received three in-person health-counseling sessions, one home visit, and five telephone calls using motivational interviewing; the comparison group received an equal number of contacts to discuss topics on healthy aging. The primary outcome was change in sitting time at 12 weeks measured via thigh-worn inclinometers (ActivPAL microTM). Group differences in outcomes were analyzed using linear mixed-effects models. Results: The trial arms were comparable at baseline with mean (SD) age 65 (6.5) years, education 10.7 (4.4) years, 32.4 (4.8) kg/m2 BMI, sitting time 540 (86) minutes/day. Significant differences between control and intervention groups were observed in change in sitting time (p Conclusions: The findings demonstrate that our intervention was successful at reducing sitting time and increasing standing time and step counts. Results demonstrate that it is feasible and effective to implement sitting time reduction interventions among older, predominately Spanish-speaking postmenopausal Latinas.

Introduction: Social connectedness (SC) is an important determinant of maintaining overall health, and the strength of connections with others may influence cardiovascular health (CVH) in part through improvement in cardiovascular disease risk factors. However, there is a lack of research on the role of SC in promoting CVH, as defined by the American Heart Association’s 2020 Impact Goals. The objective of this study is to determine the effect of SC on CVH among adults. Hypothesis: Higher SC will be associated with higher odds of optimal CVH, relative to inadequate CVH. Methods: Data are from the Community of Mine study, a cross-sectional study of 602 participants (42% Hispanic, 56% female, mean age 59 years) living in San Diego County. CVH was based on Life’s Simple 7 using six metrics including smoking, physical activity, body mass index, total cholesterol, blood pressure, and fasting glucose. A 12-point summary score was used to classify participants as having inadequate (0-6), average (7-8), or optimal (9-12) CVH. SC was assessed using a 7-item scale designed to assess social relationships and strength of connections with others (Cronbach’s alpha=0.92). A mean score across all items was generated. Multinomial logistic regression was used to estimate the association between SC and CVH. We adjusted for age, race/ethnicity, governmental cash assistance, income, education, sex, history of medical comorbidities, family history of heart disease, and depressive symptoms. Results: SC was independently associated with CVH (p=0.04). More specifically, a 1-unit increase in mean SC score was associated with a 46% increase in relative odds of having optimal CVH compared to inadequate CVH (OR=1.46, 95% CI: 1.07-1.98), and a 21% increase in relative odds of having average CVH compared to inadequate CVH (OR=1.21, 95% CI: 0.96-1.52). Conclusions: Higher social connectedness is associated with a greater likelihood of having optimal CVH. Individuals’ perceptions of the strength of their relationships could have cardioprotective benefits through promotion of favorable cardiovascular health behaviors and factors.

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

Background Sedentary 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. Methods Six 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. Results Four 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. Conclusions In 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.

Cyclophosphamide is associated with chemotherapy-related ovarian failure (CROF) in breast cancer survivors, however little is known about predicting individual risks. We sought to identify genetic alleles as biomarkers for risk of CROF after cyclophosphamide treatment. 115 premenopausal women with newly diagnosed breast cancer were genotyped for single nucleotide polymorphisms (SNPs) in genes involved in cyclophosphamide activation (CYP3A4 and CYP2C19) and detoxification (GSTP1 and GSTA1). Patients prospectively completed menstrual diaries. With median follow up of 808 days, 28% experienced CROF. Survivors homozygous for the GSTA1 minor allele had lower hazards for developing CROF (HR 0.22 [95% CI 0.05–0.94], p=0.04), while survivors homozygous for the CYP2C19 minor allele had higher hazards for developing CROF (HR 4.5 [95% CI 1.5–13.4], p=0.007) compared to patients with at least one major allele. In separate multivariable models adjusting for age and tamoxifen use, the associations were attenuated (GSTA1 HR 0.24 [95% CI 0.06–1.0], p=0.05; CYP2C19 HR 2.5 [0.8–7.6], p=0.11). CYP3A4 and GSTP1 SNPs were not significantly related to CROF. In younger breast cancer survivors undergoing cyclophosphamide-based chemotherapy, genetic variation in CYP2C19 and GSTA1 may be associated CROF.

Rationale & Objective Biomarkers 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 Design Prospective cohort. Setting & Participants 1,001 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. Predictors 13 urine metabolites, age, race, sex, smoked more than 100 cigarettes in lifetime, body mass index, hemoglobin A1c level, blood pressure, urinary albumin, and eGFR. Outcomes Annual eGFR slope and time to incident kidney failure with replacement therapy (KFRT; ie, initiation of dialysis or receipt of transplant). Analytical Approach Several 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. Results During 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]). Limitations Subgroups for whom metabolite signatures may not be optimal, nontargeted metabolomics by flow-injection analysis, and 2-stage modeling approaches. Conclusions Urine 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.

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/m 2 ). 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 P =0.02) mean sitting bout duration. Among all women, longer total sitting time was deleteriously associated with fasting insulin and triglyceride concentrations, insulin resistance, body mass index and waist circumference; longer mean sitting bout duration was deleteriously associated with fasting glucose and insulin concentrations, insulin resistance, body mass index and waist circumference. Exploratory interaction analysis showed that the association between mean sitting bout duration and fasting glucose concentration was significantly stronger among Hispanic women than non‐Hispanic women ( P ‐interaction=0.03). Conclusions Ethnic differences in 2 objectively measured parameters of sitting behavior, as well as detrimental associations between parameters and cardiometabolic biomarkers were observed in overweight/obese older women. The detrimental association between mean sitting bout duration and fasting glucose may be greater in Hispanic women than in non‐Hispanic women. Corroboration in larger studies is warranted.

Context Many female survivors of adolescent and young adult cancers (AYA survivors) have shortened reproductive lifespans. However, the timing and duration of ovarian function after cancer treatment are largely unknown. Objective To model the trajectory of ovarian function over two decades following cancer treatment and evaluate how trajectories vary by treatment gonadotoxicity and age. Design In a prospective cohort, AYA survivors aged 18-39 at variable times since cancer treatment completion provided dried blood spots (DBS) every 6 months for up to 18 months. Anti-Müllerian hormone (AMH) levels were measured using the Ansh DBS AMH enzyme-linked immunosorbent assay. The mean AMH trajectory was modeled for the entire cohort and separately by treatment gonadotoxicity and age using functional principal components analysis. Results 763 participants, mean (standard deviation) enrollment age 33.3 (4.7) and age at cancer diagnosis 25.9 (5.7) years, contributed 1905 DBS samples. The most common cancers were breast (26.9%), lymphoma (24.8%), and thyroid (18.0%). AMH trajectories differed among survivors by treatment gonadotoxicity (low, moderate, or high) (P Conclusions In this large AYA survivor cohort, timing and duration of ovarian function strongly depended on treatment gonadotoxicity and age at treatment. The findings provide novel, more precise information to guide reproductive decision-making.

IMPORTANCE: Guidelines endorsing vegetable-enriched diets to improve outcomes for prostate cancer survivors are based on expert opinion, preclinical studies, and observational data. OBJECTIVE: To determine the effect of a behavioral intervention that increased vegetable intake on cancer progression in men with early-stage prostate cancer. DESIGN, SETTING, AND PARTICIPANTS: The 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

Background: Individuals with type 1 diabetes (T1D) demonstrate varied trajectories of estimated glomerular filtration rate (eGFR) decline. The molecular pathways underlying rapid eGFR decline in T1D are poorly understood, and individual-level risk of rapid eGFR decline is difficult to predict. Methods: We designed a case-control study with multiple exposure measurements nested within 4 well-characterized T1D cohorts (FinnDiane, Steno, EDC, and CACTI) to identify biomarkers associated with rapid eGFR decline. Here, we report the rationale for and design of these studies as well as results of models testing associations of clinical characteristics with rapid eGFR decline in the study population, upon which “omics” studies will be built. Cases (n = 535) and controls (n = 895) were defined as having an annual eGFR decline of ≥3 and 2, respectively. Associations of demographic and clinical variables with rapid eGFR decline were tested using logistic regression, and prediction was evaluated using area under the curve (AUC) statistics. Targeted metabolomics, lipidomics, and proteomics are being performed using high-resolution mass-spectrometry techniques. Results: At baseline, the mean age was 43 years, diabetes duration was 27 years, eGFR was 94 mL/min/1.73 m2, and 62% of participants were normoalbuminuric. Over 7.6-year median follow-up, the mean annual change in eGFR in cases and controls was −5.7 and 0.6 mL/min/1.73 m2, respectively. Younger age, longer diabetes duration, and higher baseline HbA1c, urine albumin-creatinine ratio, and eGFR were significantly associated with rapid eGFR decline. The cross-validated AUC for the predictive model incorporating these variables plus sex and mean arterial blood pressure was 0.74 (95% CI: 0.68–0.79; p < 0.001). Conclusion: Known risk factors provide moderate discrimination of rapid eGFR decline. Identification of blood and urine biomarkers associated with rapid eGFR decline in T1D using targeted omics strategies may provide insight into disease mechanisms and improve upon clinical predictive models using traditional risk factors.

Author(s): Young, Katie; Shliakhtsitsava, Ksenya; Natarajan, Loki; Myers, Emily; Dietz, Andrew C; Gorman, Jessica R; Martinez, Maria Elena; Whitcomb, Brian W; Su, H Irene | Abstract: BackgroundFertility counseling before cancer treatment has been advocated by clinical guidelines, though little is known about its long-term impact on the unique reproductive concerns of female adolescent and young adult (AYA) cancer survivors. The goal of this study was to measure the association between fertility counseling by fertility specialists before cancer treatment and subsequent reproductive concerns.MethodA cross-sectional analysis was performed among 747 AYA survivors aged 18-40 years who had been recruited from cancer registries and physician and advocacy group referrals between 2015 and 2017. Participants self-reported information on past fertility counseling at cancer diagnosis, cancer type and treatment, and current reproductive concerns, as measured using the multidimensional Reproductive Concerns After Cancer scale. Multivariable log-binomial regression models tested associations between fertility counseling and reproductive concerns.ResultsThe mean age of the cohort was 33.0 years (standard deviation, 5.1 years), and the mean period since diagnosis was 7.7 years (standard deviation, 5.0 years). Seventy-three percent of participants were white, and 24% were Hispanic. Fertility counseling was reported by 19% of survivors; moderate to high overall reproductive concerns were reported by 44% of participants. In adjusted analysis, fertility counseling was significantly associated with moderate to high reproductive concerns (risk ratio, 1.22; 95% confidence interval, 1.02-1.45) and not modified by exposure to fertility-threatening treatments (Pinteraction = .23).ConclusionA large proportion of AYA cancer survivors across cancer types and treatment exposures reported moderate to high reproductive concerns, suggesting that there is a need to address these cancer-specific reproductive health concerns after treatment. Higher concerns, even with counseling, suggests the need to improve the quality of fertility counseling throughout the cancer continuum.

PURPOSE: To examine associations between dimensions of sedentary behavior and cognitive function in breast cancer survivors. METHODS: Sedentary behavior variables were measured using thigh-worn activPALs, and included total daily sitting time, time in long sitting bouts, sit-to-stand transitions, and standing time. Cognitive functioning was assessed using the NIH Toolbox Cognitive Domain. Separate multivariable linear regression models were used to examine associations between sedentary behavior variables with the cognitive domain scores of attention, executive functioning, episodic memory, working memory, and information processing speed. RESULTS: Thirty breast cancer survivors with a mean age of 62.2 (SD=7.8) years who were 2.6 (SD=1.1) years since diagnosis completed study assessments. In multivariable linear regression models, more time spent standing was associated with faster information processing (b: 5.78; p=0.03), and more time spent in long sitting bouts was associated with worse executive function (b: −2.82; p=0.02), after adjustment for covariates. No other sedentary behavior variables were statistically significantly associated with the cognitive domains examined in this study. CONCLUSIONS: Two important sedentary constructs that are amenable to intervention, including time in prolonged sitting bouts and standing time, may be associated with cognitive function in breast cancer survivors. More research is needed to determine whether modifying these dimensions of sedentary behavior will improve cognitive function in women with a history of breast cancer, or prevent it from declining in breast cancer patients.

Background Older adults are the least active population group. Interventions in residential settings may support a multi-level approach to behavior change. Methods In a cluster randomized control trial, 11 San Diego retirement communities were assigned to a physical activity (PA) intervention or a healthy aging attention control condition. Participants were 307 adults over 65 years old. The multilevel PA intervention was delivered with the assistance of peer leaders, who were trained older adult from the retirement communities. Intervention components included individual counseling & self-monitoring with pedometers, group education sessions, group walks, community advocacy and pedestrian community change projects. Intervention condition by time interactions were tested using generalized mixed effects regressions. The primary outcomes was accelerometer measured physical activity. Secondary outcomes were blood pressure and objectively measured physical functioning. Results Over 70% of the sample were 80 years or older. PA significantly increased in the intervention condition (56 min of moderate-vigorous PA per week; 119 min of light PA) compared with the control condition and remained significantly higher across the 12 month study. Men and participants under 84 years old benefited most from the intervention. There was a significant decrease in systolic (p