Host variables confound gut microbiota studies of human disease.

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.

Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors.

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 ß ^ : 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.

Arriba por la Vida Estudio: a randomized controlled trial promoting standing behavior to reduce sitting time among postmenopausal Latinas.

Postmenopausal Hispanic/Latina (N = 254) women with a body mass index (BMI) ≥ 25 kg/m2 were randomized to an intervention to reduce sitting time or a comparison condition for 12 weeks. The standing intervention group received three in-person health-counseling sessions, one home visit, and up to eight motivational interviewing calls. The heart healthy lifestyle comparison group (C) received an equal number of contact hours to discuss healthy aging. The primary outcome was 12-week change in sitting time measured via thigh-worn activPAL. Group differences in outcomes were analyzed using linear mixed-effects models. Participants had a mean age of 65 (6.5) years, preferred Spanish language (89%), BMI of 32.4 (4.8) kg/m2, and sat for an average of 540 (86) minutes/day. Significant between-group differences were observed in reductions of sitting time across the 12-week period [Mdifference (SE): C - 7.5 (9.1), SI - 71.0 (9.8), p < 0.01]. Results demonstrate that coaching models to reduce sitting are feasible and effective.

A generalized covariate-adjusted top-scoring pair algorithm with applications to diabetic kidney disease stage classification in the Chronic Renal Insufficiency Cohort (CRIC) Study.

BACKGROUND: The 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. RESULTS: Our 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. CONCLUSIONS: We 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.

Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE).

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 < 0.001) and higher SB breaks (b = -0.005--0.052; all p < 0.001). Lower total SB was unexpectedly related to greater obesity (b = -0.077--0.649; p from <0.001-0.02). For mean SB bout duration and SB breaks, more associations were observed for SBposture (n = 5) than for SBmovement (n = 3) or SBcombined (n = 1), and tended to have larger magnitude as well. CONCLUSIONS: Using traditional measures of low movement as a surrogate for SB may lead to underestimated or undetected adverse associations between SB and obesity. CHAP-Child allows assessment of sitting posture using hip-worn accelerometers. Ongoing work is needed to understand how low movement and posture are related to one another, as well as their potential health implications.

High-Throughput Metabolomics and Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency (CRIC) Study.

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.

Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data.

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.

Health effects and cost-effectiveness of a multilevel physical activity intervention in low-income older adults; results from the PEP4PA cluster randomized controlled trial.

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.

CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children.

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.

Reductions in sleep quality and circadian activity rhythmicity predict longitudinal changes in objective and subjective cognitive functioning in women treated for breast cancer.

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.

Psychosocial stress and ovarian function in adolescent and young adult cancer survivors.

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.

Drug metabolising enzyme polymorphisms and chemotherapy-related ovarian failure in young breast cancer survivors.

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. One hundred fifteen 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=.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=.007) compared to patients with at least one major allele. In separate multivariable models adjusting for age and tamoxifen use, the associations were no longer statistically significant (GSTA1 HR 0.24 [95% CI 0.06-1.0], p=.05; CYP2C19 HR 2.5 [0.8-7.6], p=.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 merits further study to determine its relationship with CROF.IMPACT STATEMENTWhat is already known on this subject? Young breast cancer survivors face important potential implications of chemotherapy-related ovarian failure (CROF). Little is known about individual risk for CROF. Cyclophosphamide, a particularly gonadotoxic drug commonly used in breast cancer treatment, is metabolised by various cytochrome p450 enzymes. Studies have shown genetic variation in p450 enzymes is associated with differential clinical outcomes after cyclophosphamide treatment: breast cancer patients homozygous for GSTA1 minor allele had improved overall survival; lupus patients homozygous for CYP2C19 minor allele had increased risk for CROF; and CYP3A4*1B I was associated with decreased risk for CROF.What do the results of this study add? We show a surprising opposite trend for the risk of CROF in breast cancer patients with GSTA1 and CYP2C19 variants, while we did not show a significant risk for genetic variation in CYP3A4 (which had previously been shown to have a protective effect) or GSTP1.What are the implications of these findings for clinical practice and/or further research? This study shows the complexity of genetic variation in predicting outcomes to treatment. We advocate for future replicative studies to potentially validate GSTA1 and CYP2C19 and definitively negate CYP3A4 and GSTP1 as biomarkers for risk of CROF after cyclophosphamide treatment. Understanding genetic variation in chemotherapy metabolism has the potential to individualise treatment regimens to maximise efficacy and minimise toxicity.

Research-based PAM50 signature and long-term breast cancer survival.

PURPOSE: Multi-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. METHODS: We 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. RESULTS: Over 15 + years of follow-up, PAM50 subtypes were (P < 0.01) associated with breast cancer outcomes after accounting for tumor stage, grade and age at diagnosis. Results did not differ by menopausal status at diagnosis. Women with Luminal B (versus Luminal A) subtype had a > 60% higher hazard. Addition of a 13-gene hypoxia signature improved prognostication with > 40% higher hazard in the highest vs lowest hypoxia tertiles. CONCLUSIONS: PAM50 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.

Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease.

MOTIVATION: Functional enrichment testing methods can reduce data comprising hundreds of altered biomolecules to smaller sets of altered biological 'concepts' that help generate testable hypotheses. This study leveraged differential network enrichment analysis methodology to identify and validate lipid subnetworks that potentially differentiate chronic kidney disease (CKD) by severity or progression. RESULTS: We built a partial correlation interaction network, identified highly connected network components, applied network-based gene-set analysis to identify differentially enriched subnetworks, and compared the subnetworks in patients with early-stage versus late-stage CKD. We identified two subnetworks 'triacylglycerols' and 'cardiolipins-phosphatidylethanolamines (CL-PE)' characterized by lower connectivity, and a higher abundance of longer polyunsaturated triacylglycerols in patients with severe CKD (stage ≥4) from the Clinical Phenotyping Resource and Biobank Core. These finding were replicated in an independent cohort, the Chronic Renal Insufficiency Cohort. Using an innovative method for elucidating biological alterations in lipid networks, we demonstrated alterations in triacylglycerols and cardiolipins-phosphatidylethanolamines that precede the clinical outcome of end-stage kidney disease by several years. AVAILABILITY AND IMPLEMENTATION: A complete list of NetGSA results in HTML format can be found at http://metscape.ncibi.org/netgsa/12345-022118/cric_cprobe/022118/results_cric_cprobe/main.html. The DNEA is freely available at https://github.com/wiggie/DNEA. Java wrapper leveraging the cytoscape.js framework is available at http://js.cytoscape.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study.

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.

A Targeted Multiomics Approach to Identify Biomarkers Associated with Rapid eGFR Decline in Type 1 Diabetes.

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 <1 mL/min/1.73 m2, 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.

Diurnal patterns of sedentary behavior and changes in physical function over time among older women: a prospective cohort study.

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.

Effect of a Behavioral Intervention to Increase Vegetable Consumption on Cancer Progression Among Men With Early-Stage Prostate Cancer: The MEAL Randomized Clinical Trial.

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 <70 years and ≤2 in those ≥70 years), stage cT2a or less, and serum prostate-specific antigen (PSA) level less than 10 ng/mL. Enrollment occurred from January 2011 to August 2015; 24-month follow-up occurred from January 2013 to August 2017. Interventions: Patients were randomized to a counseling behavioral intervention by telephone promoting consumption of 7 or more daily vegetable servings (MEAL intervention; n = 237) or a control group, which received written information about diet and prostate cancer (n = 241). Main Outcomes and Measures: The primary outcome was time to progression; progression was defined as PSA level of 10 ng/mL or greater, PSA doubling time of less than 3 years, or upgrading (defined as increase in tumor volume or grade) on follow-up prostate biopsy. Results: Among 478 patients randomized (mean [SD] age, 64 [7] years; mean [SD] PSA level, 4.9 [2.1] ng/mL), 443 eligible patients (93%) were included in the primary analysis. There were 245 progression events (intervention: 124; control: 121). There were no significant differences in time to progression (unadjusted hazards ratio, 0.96 [95% CI, 0.75 to 1.24]; adjusted hazard ratio, 0.97 [95% CI, 0.76 to 1.25]). The 24-month Kaplan-Meier progression-free percentages were 43.5% [95% CI, 36.5% to 50.6%] and 41.4% [95% CI, 34.3% to 48.7%] for the intervention and control groups, respectively (difference, 2.1% [95% CI, -8.1% to 12.2%]). Conclusions and Relevance: Among men with early-stage prostate cancer managed with active surveillance, a behavioral intervention that increased vegetable consumption did not significantly reduce the risk of prostate cancer progression. The findings do not support use of this intervention to decrease prostate cancer progression in this population, although the study may have been underpowered to identify a clinically important difference. Trial Registration: ClinicalTrials.gov Identifier: NCT01238172.

Fertility counseling before cancer treatment and subsequent reproductive concerns among female adolescent and young adult cancer survivors.

BACKGROUND: Fertility 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. METHOD: A 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. RESULTS: The 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). CONCLUSION: A 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.

Efficacy of a web-based women's health survivorship care plan for young breast cancer survivors: a randomized controlled trial.

PURPOSE: Breast cancer survivorship care plans (SCP) have limited content addressing women's health issues. This trial tested if young breast cancer survivors who receive a web-based, women's health SCP were more likely to improve on at least one of the four targeted issues (hot flashes, fertility-related concerns, contraception, and vaginal symptoms) compared to attention controls. METHODS: A randomized controlled trial recruited female survivors ages 18-45 at diagnosis, 18-50 at enrollment, completed primary cancer treatment, and had a significant women's health issue: moderate or higher fertility-related concerns; ≥ 4 hot flashes/day with ≥ 1 of moderate severity; ≥ 1 moderate vaginal atrophy symptoms; or not contracepting/using less effective methods. Survivors underwent stratified, block randomization with equal allocation to intervention and control groups. The intervention group accessed the online SCP; controls accessed curated resource lists. In intention-to-treat analysis, the primary outcome of improvement in at least one issue by 24 weeks was compared by group. RESULTS: 182 participants (86 intervention, 96 control), mean age 40.0 ± 5.9 and 4.4 ± 3.2 years since diagnosis, were randomized. 61 intervention group participants (70.9%) improved, compared to 55 controls (57.3%) (OR 1.82, 95% CI 0.99-3.4, p = 0.057). The following issue-specific improvements were observed in the intervention versus control arms: fertility-related concerns (27.9% vs. 14.6%, OR 2.3, 95% CI 1.1-4.8); hot flashes (58.5% vs. 55.8%, OR 1.1, 95% CI 0.57-2.2); vaginal symptoms (42.5% vs. 40.7%, OR 1.1, 95% CI 0.6-2.0); contraception (50% vs. 42.6%, OR 1.4, 95% CI 0.74-2.5). CONCLUSIONS: In young breast cancer survivors, a novel, web-based SCP did not result in more change in the primary outcome of improvement in at least one of the four targeted women's health issues, than the attention control condition. The intervention was associated with improved infertility concerns, supporting efficacy of disseminating accessible, evidence-based women's health information to this population.