Interspecies interactions determine growth dynamics of biopolymer-degrading populations in microbial communities.

Microbial communities perform essential ecosystem functions such as the remineralization of organic carbon that exists as biopolymers. The first step in mineralization is performed by biopolymer degraders, which harbor enzymes that can break down polymers into constituent oligo- or monomeric forms. The released nutrients not only allow degraders to grow, but also promote growth of cells that either consume the degradation products, i.e., exploiters, or consume metabolites released by the degraders or exploiters, i.e., scavengers. It is currently not clear how such remineralizing communities assemble at the microscale-how interactions between the different guilds influence their growth and spatial distribution, and hence the development and dynamics of the community. Here, we address this knowledge gap by studying marine microbial communities that grow on the abundant marine biopolymer alginate. We used batch growth assays and microfluidics coupled to time-lapse microscopy to quantitatively investigate growth and spatial distribution of single cells. We found that the presence of exploiters or scavengers alters the spatial distribution of degrader cells. In general, exploiters and scavengers-which we collectively refer to as cross-feeder cells-slowed down the growth of degrader cells. In addition, coexistence with cross-feeders altered the production of the extracellular enzymes that break down polymers by degrader cells. Our findings reveal that ecological interactions by nondegrading community members have a profound impact on the functions of microbial communities that remineralize carbon biopolymers in nature.

A Bayesian nonparametric approach for multiple mediators with applications in mental health studies.

Mediation analysis with contemporaneously observed multiple mediators is a significant area of causal inference. Recent approaches for multiple mediators are often based on parametric models and thus may suffer from model misspecification. Also, much of the existing literature either only allow estimation of the joint mediation effect or estimate the joint mediation effect just as the sum of individual mediator effects, ignoring the interaction among the mediators. In this article, we propose a novel Bayesian nonparametric method that overcomes the two aforementioned drawbacks. We model the joint distribution of the observed data (outcome, mediators, treatment, and confounders) flexibly using an enriched Dirichlet process mixture with three levels. We use standardization (g-computation) to compute all possible mediation effects, including pairwise and all other possible interaction among the mediators. We thoroughly explore our method via simulations and apply our method to a mental health data from Wisconsin Longitudinal Study, where we estimate how the effect of births from unintended pregnancies on later life mental depression (CES-D) among the mothers is mediated through lack of self-acceptance and autonomy, employment instability, lack of social participation, and increased family stress. Our method identified significant individual mediators, along with some significant pairwise effects.

Clinical and Adverse Outcomes Associated With Concomitant Use of CYP2D6-Metabolized Opioids With Antidepressants in Older Nursing Home Residents : A Target Trial Emulation Study.

BACKGROUND: Limited evidence exists on the safety of pharmacokinetic interactions of cytochrome P450 (CYP) 2D6 (CYP2D6)-metabolized opioids with antidepressants among older nursing home (NH) residents. OBJECTIVE: To investigate the associations of concomitant use of CYP2D6-metabolized opioids and antidepressants with clinical outcomes and opioid-related adverse events (ORAEs). DESIGN: Retrospective cohort study using a target trial emulation framework. SETTING: 100% Medicare NH sample linked to Minimum Data Set (MDS) from 2010 to 2021. PARTICIPANTS: Long-term residents aged 65 years and older receiving CYP2D6-metabolized opioids with a disease indication for antidepressant use. INTERVENTION: Initiating CYP2D6-inhibiting versus CYP2D6-neutral antidepressants that overlapped with use of CYP2D6-metabolized opioids for 1 day or more. MEASUREMENTS: Clinical outcomes were worsening pain, physical function, and depression from baseline to quarterly MDS assessments and were analyzed using modified Poisson regression models. The ORAE outcomes included counts of pain-related hospitalizations and emergency department (ED) visits, opioid use disorder (OUD), and opioid overdose and were analyzed with negative binomial or Poisson regression models. All models were adjusted for baseline covariates via inverse probability of treatment weighting. RESULTS: Among 29 435 identified residents, use of CYP2D6-metabolized opioids concomitantly with CYP2D6-inhibiting (vs. CYP2D6-neutral) antidepressants was associated with a higher adjusted rate ratio of worsening pain (1.13 [95% CI, 1.09 to 1.17]) and higher adjusted incidence rate ratios of pain-related hospitalization (1.37 [CI, 1.19 to 1.59]), pain-related ED visit (1.49 [CI, 1.24 to 1.80]), and OUD (1.93 [CI, 1.37 to 2.73]), with no difference in physical function, depression, and opioid overdose. LIMITATION: Findings are generalizable to NH populations only. CONCLUSION: Use of CYP2D6-metabolized opioids concomitantly with CYP2D6-inhibiting (vs. CYP2D6-neutral) antidepressants was associated with worsening pain and increased risk for most assessed ORAEs among older NH residents. PRIMARY FUNDING SOURCE: National Institute on Aging.

Comparative analysis of circulating metabolomic profiles identifies shared metabolic alterations across distinct multistressor military training exercises.

Military training provides insight into metabolic responses under unique physiological demands that can be comprehensively characterized by global metabolomic profiling to identify potential strategies for improving performance. This study identified shared changes in metabolomic profiles across three distinct military training exercises, varying in magnitude and type of stress. Blood samples collected before and after three real or simulated military training exercises were analyzed using the same untargeted metabolomic profiling platform. Exercises included a 2-wk survival training course (ST, n = 36), a 4-day cross-country ski march arctic training (AT, n = 24), and a 28-day controlled diet- and exercise-induced energy deficit (CED, n = 26). Log2-fold changes of greater than ±1 in 191, 121, and 64 metabolites were identified in the ST, AT, and CED datasets, respectively. Most metabolite changes were within the lipid (57-63%) and amino acid metabolism (18-19%) pathways and changes in 87 were shared across studies. The largest and most consistent increases in shared metabolites were found in the acylcarnitine, fatty acid, ketone, and glutathione metabolism pathways, whereas the largest decreases were in the diacylglycerol and urea cycle metabolism pathways. Multiple shared metabolites were consistently correlated with biomarkers of inflammation, tissue damage, and anabolic hormones across studies. These three studies of real and simulated military training revealed overlapping alterations in metabolomic profiles despite differences in environment and the stressors involved. Consistent changes in metabolites related to lipid metabolism, ketogenesis, and oxidative stress suggest a potential common metabolomic signature associated with inflammation, tissue damage, and suppression of anabolic signaling that may characterize the unique physiological demands of military training.NEW & NOTEWORTHY The extent to which metabolomic responses are shared across diverse military training environments is unknown. Global metabolomic profiling across three distinct military training exercises identified shared metabolic responses with the largest changes observed for metabolites related to fatty acids, acylcarnitines, ketone metabolism, and oxidative stress. These changes also correlated with alterations in markers of tissue damage, inflammation, and anabolic signaling and comprise a potential common metabolomic signature underlying the unique physiological demands of military training.

A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout.

Studies of memory trajectories using longitudinal data often result in highly nonrepresentative samples due to selective study enrollment and attrition. An additional bias comes from practice effects that result in improved or maintained performance due to familiarity with test content or context. These challenges may bias study findings and severely distort the ability to generalize to the target population. In this study, we propose an approach for estimating the finite population mean of a longitudinal outcome conditioning on being alive at a specific time point. We develop a flexible Bayesian semiparametric predictive estimator for population inference when longitudinal auxiliary information is known for the target population. We evaluate the sensitivity of the results to untestable assumptions and further compare our approach to other methods used for population inference in a simulation study. The proposed approach is motivated by 15-year longitudinal data from the Betula longitudinal cohort study. We apply our approach to estimate lifespan trajectories in episodic memory, with the aim to generalize findings to a target population.

Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research.

Joint modeling of longitudinal data such as quality of life data and survival data is important for palliative care researchers to draw efficient inferences because it can account for the associations between those two types of data. Modeling quality of life on a retrospective from death time scale is useful for investigators to interpret the analysis results of palliative care studies which have relatively short life expectancies. However, informative censoring remains a complex challenge for modeling quality of life on the retrospective time scale although it has been addressed for joint models on the prospective time scale. To fill this gap, we develop a novel joint modeling approach that can address the challenge by allowing informative censoring events to be dependent on patients' quality of life and survival through a random effect. There are two sub-models in our approach: a linear mixed effect model for the longitudinal quality of life and a competing-risk model for the death time and dropout time that share the same random effect as the longitudinal model. Our approach can provide unbiased estimates for parameters of interest by appropriately modeling the informative censoring time. Model performance is assessed with a simulation study and compared with existing approaches. A real-world study is presented to illustrate the application of the new approach.

Flexible evaluation of surrogacy in platform studies.

Trial-level surrogates are useful tools for improving the speed and cost effectiveness of trials but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial-level surrogate evaluation, but none, to our knowledge, for the specific setting of platform studies. As platform studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate evaluation that would not normally be possible. However, they also offer a set of statistical issues including heterogeneity of the study population, treatments, implementation, and even potentially the quality of the surrogate. We propose the use of a hierarchical Bayesian semiparametric model for the evaluation of potential surrogates using nonparametric priors for the distribution of true effects based on Dirichlet process mixtures. The motivation for this approach is to flexibly model relationships between the treatment effect on the surrogate and the treatment effect on the outcome and also to identify potential clusters with differential surrogate value in a data-driven manner so that treatment effects on the surrogate can be used to reliably predict treatment effects on the clinical outcome. In simulations, we find that our proposed method is superior to a simple, but fairly standard, hierarchical Bayesian method. We demonstrate how our method can be used in a simulated illustrative example (based on the ProBio trial), in which we are able to identify clusters where the surrogate is, and is not useful. We plan to apply our method to the ProBio trial, once it is completed.

Variable Selection Using Bayesian Additive Regression Trees.

Variable selection is an important statistical problem. This problem becomes more challenging when the candidate predictors are of mixed type (e.g. continuous and binary) and impact the response variable in nonlinear and/or non-additive ways. In this paper, we review existing variable selection approaches for the Bayesian additive regression trees (BART) model, a nonparametric regression model, which is flexible enough to capture the interactions between predictors and nonlinear relationships with the response. An emphasis of this review is on the ability to identify relevant predictors. We also propose two variable importance measures which can be used in a permutation-based variable selection approach, and a backward variable selection procedure for BART. We introduce these variations as a way of illustrating limitations and opportunities for improving current approaches and assess these via simulations.

A Bayesian nonparametric approach for causal mediation with a post-treatment confounder.

We propose a new Bayesian nonparametric method for estimating the causal effects of mediation in the presence of a post-treatment confounder. The methodology is motivated by the Rural Lifestyle Intervention Treatment Effectiveness Trial (Rural LITE) for which there is interest in estimating causal mediation effects but is complicated by the presence of a post-treatment confounder. We specify an enriched Dirichlet process mixture (EDPM) to model the joint distribution of the observed data (outcome, mediator, post-treatment confounder, treatment, and baseline confounders). For identifiability, we use the extended version of the standard sequential ignorability (SI) as introduced in Hong et al. along with a Gaussian copula model assumption. The observed data model and causal identification assumptions enable us to estimate and identify the causal effects of mediation, that is, the natural direct effects (NDE) and natural indirect effects (NIE). Our method enables easy computation of NIE and NDE for a subset of confounding variables and addresses missing data through data augmentation under the assumption of ignorable missingness. We conduct simulation studies to assess the performance of our proposed method. Furthermore, we apply this approach to evaluate the causal mediation effect in the Rural LITE trial, finding that there was not strong evidence for the potential mediator.

Regular cannabis smoking and carotid artery calcification in the Multi-Ethnic Study of Atherosclerosis (MESA).

BACKGROUND: Studies on cannabis use and adverse cardiovascular outcomes have reported conflicting results. Research on its relationship to calcified arterial plaque remains limited. METHODS: Cross-sectional data from 2152 participants at Exam 6 (2016-2018) in the Multi-Ethnic Study of Atherosclerosis (MESA) were analyzed, including self-reported cannabis smoking patterns and carotid artery calcification (CAC) as measured via computed tomography. Multivariable relative and absolute risk regression models were used to estimate adjusted prevalence ratios (PRs) and prevalence differences, respectively, for the presence of calcified plaque. Multivariable linear regression was then used to compare group differences in the extent of CAC in those with calcified plaque. RESULTS: A minority of participants (n = 159, 7.4%) reported a history of regular cannabis smoking. Among all participants, 36.1% (n = 777) had detectable CAC. In models adjusted for demographics, behavioral, and clinical cardiovascular disease factors, a history of regular cannabis smoking was not associated with the prevalence of CAC in either common carotid artery (PR: 1.14, 95% CI: 0.88 to 1.49). In the subset of participants with calcified plaque, and in separate fully adjusted multivariable linear regression models, a history of regular cannabis smoking was not associated with increased calcium volume (difference = 7.7%, 95% CI: -21.8 to 48.5), calcium density (difference = 0.4%, 95% CI: -6.6 to 7.9), or Agatston score (difference = 32.1%, 95% CI: -31.8 to 155.8) in either carotid artery. Models exploring potential effect modification by age, race/ethnicity, and tobacco smoking status showed no significant association, except for higher CAC prevalence in men with a history of regular cannabis smoking. CONCLUSIONS: In a racially and ethnically diverse cohort of older adults with a moderately high prevalence of CAC, no associations were found between a history of regular cannabis smoking, duration, or recency of cannabis smoking, and the prevalence of carotid calcified plaque. These findings were consistent across age, race/ethnicity, and cigarette smoking, except for an increased prevalence in men with a history of regular cannabis smoking. Similarly, in a subgroup with CAC, no association was found between a history of regular cannabis smoking and extent of calcification as measured by volume, density, and Agatston score.

Short- and long-term safety of discontinuing chronic opioid therapy among older adults with Alzheimer's disease and related dementia.

BACKGROUND: Limited evidence exists on the short- and long-term safety of discontinuing versus continuing chronic opioid therapy (COT) among patients with Alzheimer's disease and related dementias (ADRD). METHODS: This cohort study was conducted among 162,677 older residents with ADRD and receipt of COT using a 100% Medicare nursing home sample. Discontinuation of COT was defined as no opioid refills for ≥90 days. Primary outcomes were rates of pain-related hospitalisation, pain-related emergency department visit, injury, opioid use disorder (OUD) and opioid overdose (OD) measured by diagnosis codes at quarterly intervals during 1- and 2-year follow-ups. Poisson regression models were fit using generalised estimating equations with inverse probability of treatment weights to model quarterly outcome rates between residents who discontinued versus continued COT. RESULTS: The study sample consisted of 218,040 resident episodes with COT; of these episodes, 180,916 residents (83%) continued COT, whereas 37,124 residents (17%) subsequently discontinued COT. Discontinuing (vs. continuing) COT was associated with higher rates of all outcomes in the first quarter, but these associations attenuated over time. The adjusted rates of injury, OUD and OD were 0, 69 and 60% lower at the 1-year follow-up and 11, 81 and 79% lower at the 2-year follow-up, respectively, for residents who discontinued versus continued COT, with no difference in the adjusted rates of pain-related hospitalisations or emergency department visits. CONCLUSIONS: The rates of adverse outcomes were higher in the first quarter but lower or non-differential at 1-year and 2-year follow-ups between COT discontinuers versus continuers among older residents with ADRD.

Difference in Cyclic Versus Non-cyclic Symptom Patterns in Patients with the Symptoms of Gastroparesis Undergoing Bioelectric Therapy.

INTRODUCTION: Patients with gastroparesis (Gp) have symptoms with or without a cyclic pattern. This retrospective study evaluates differences in cyclic vs. non-cyclic symptoms of Gp by analyzing mucosal electrogastrogram (mEG), familial dysautonomias, and response to gastric stimulation. METHODS: 37 patients with drug refractory Gp, 7 male and 30 female, with a mean age of 41.4 years, were studied. 18 had diabetes mellitus, 25 had cyclic (Cyc), and 12 had a non-cyclic (NoCyc) pattern of symptoms. Patients underwent temporary mucosal gastric stimulator (tGES) placement, which was done as a trial before permanent stimulator (GES) placement. Electrogastrogram (EGG) by mucosal (mEG) measures, including frequency, amplitude, and frequency-amplitude ratio (FAR), were pre- and post-tGES. Patients' history of personal and familial dysautonomias, quality of life, and symptom scores were recorded. Baseline vs. follow-ups were compared by paired t tests and McNemar's tests. T tests contrasted symptom scores, gastric emptying tests (GET), and mEG measures, while chi-squared tests deciphered comorbidity differences between two groups and univariate and multivariate analyses. RESULTS: There were significantly more patients with diabetes in the Cyc group vs. the NoCyc group. Using a 1 point in symptom outcome, 18 patients did not improve and 19 did improve with tGES. Using univariable analysis, with the cyclic pattern as a predictor, patients exhibiting a cyclic pattern had an odds ratio of 0.22 (95% CI 0.05-0.81, p = 0.054) for achieving an improvement of at least one unit in vomiting at follow-up from baseline. The mucosal electrogastrogram frequency to amplitude ratio (FAR) for the "not Improved" group was 19.6 [3.5, 33.6], whereas, for the "Improved" group, it was 54.3 [25.6, 72.5] with a p-value of 0.049. For multivariate logistic regression, accounting for sex and age squared, patients exhibiting a cyclic pattern had an adjusted odds ratio (OR) of 0.16 (95% CI 0.03-0.81, p = 0.027) for achieving an improvement of at least one unit in vomiting at follow-up from baseline. The two groups had no significant differences in the personal or inherited history of investigated familial patterns. CONCLUSION: This study shows differences in Gp patients with Cyc vs. NoCyc symptoms in several areas. Larger studies are needed to elicit further differences between the two groups about cycles of symptoms, EGG, findings, familial patterns, and response to mucosal GES.

Five multivariate Duchenne muscular dystrophy progression models bridging six-minute walk distance and MRI relaxometry of leg muscles.

The study aimed to provide quantitative information on the utilization of MRI transverse relaxation time constant (MRI-T2) of leg muscles in DMD clinical trials by developing multivariate disease progression models of Duchenne muscular dystrophy (DMD) using 6-min walk distance (6MWD) and MRI-T2. Clinical data were collected from the prospective and longitudinal ImagingNMD study. Disease progression models were developed by a nonlinear mixed-effect modeling approach. Univariate models of 6MWD and MRI-T2 of five muscles were developed separately. Age at assessment was the time metric. Multivariate models were developed by estimating the correlation of 6MWD and MRI-T2 model variables. Full model estimation approach for covariate analysis and five-fold cross validation were conducted. Simulations were performed to compare the models and predict the covariate effects on the trajectories of 6MWD and MRI-T2. Sigmoid Imax and Emax models best captured the profiles of 6MWD and MRI-T2 over age. Steroid use, baseline 6MWD, and baseline MRI-T2 were significant covariates. The median age at which 6MWD is half of its maximum decrease in the five models was similar, while the median age at which MRI-T2 is half of its maximum increase varied depending on the type of muscle. The models connecting 6MWD and MRI-T2 successfully quantified how individual characteristics alter disease trajectories. The models demonstrate a plausible correlation between 6MWD and MRI-T2, supporting the use of MRI-T2. The developed models will guide drug developers in using the MRI-T2 to most efficient use in DMD clinical trials.

Pain intensity, physical function, and depressive symptoms associated with discontinuing long-term opioid therapy in older adults with Alzheimer's disease and related dementias.

INTRODUCTION: Limited evidence exists on the associations of discontinuing versus continuing long-term opioid therapy (LTOT) with pain intensity, physical function, and depression among patients with Alzheimer's disease and related dementias (ADRD). METHODS: A cohort study among 138,059 older residents with mild-to-moderate ADRD and receipt of LTOT was conducted using a 100% Medicare nursing home sample. Discontinuation of LTOT was defined as no opioid refills for ≥ 60 days. Outcomes were worsening pain, physical function, and depression from baseline to quarterly assessments during 1- and 2-year follow-ups. RESULTS: The adjusted odds of worsening pain and depressive symptoms were 29% and 5% lower at the 1-year follow-up and 35% and 9% lower at the 2-year follow-up for residents who discontinued versus continued LTOT, with no difference in physical function. DISCUSSION: Discontinuing LTOT was associated with lower short- and long-term worsening pain and depressive symptoms than continuing LTOT among older residents with ADRD. HIGHLIGHTS: Discontinuing long-term opioid therapy (LTOT) was associated with lower short- and long-term worsening pain. Discontinuing LTOT was related to lower short- and long-term worsening depression. Discontinuing LTOT was not associated with short- and long-term physical function.

A Bayesian nonparametric approach for evaluating the causal effect of treatment in randomized trials with semi-competing risks.

We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment in a randomized trial where a nonterminal event may be censored by a terminal event, but not vice versa (i.e., semi-competing risks). Based on the idea of principal stratification, we define a novel estimand for the causal effect of treatment on the nonterminal event. We introduce identification assumptions, indexed by a sensitivity parameter, and show how to draw inference using our BNP approach. We conduct simulation studies and illustrate our methodology using data from a brain cancer trial. The R code implementing our model and algorithm is available for download at https://github.com/YanxunXu/BaySemiCompeting.

Metformin Monotherapy Alters the Human Plasma Lipidome Independent of Clinical Markers of Glycemic Control and Cardiovascular Disease Risk in a Type 2 Diabetes Clinical Cohort.

Type 2 diabetes (T2D) is a rising pandemic worldwide. Diet and lifestyle changes are typically the first intervention for T2D. When this intervention fails, the biguanide metformin is the most common pharmaceutical therapy. Yet its full mechanisms of action remain unknown. In this work, we applied an ultrahigh resolution, mass spectrometry-based platform for untargeted plasma metabolomics to human plasma samples from a case-control observational study of nondiabetic and well-controlled T2D subjects, the latter treated conservatively with metformin or diet and lifestyle changes only. No statistically significant differences existed in baseline demographic parameters, glucose control, or clinical markers of cardiovascular disease risk between the two T2D groups, which we hypothesized would allow the identification of circulating metabolites independently associated with treatment modality. Over 3000 blank-reduced metabolic features were detected, with the majority of annotated features being lipids or lipid-like molecules. Altered abundance of multiple fatty acids and phospholipids were found in T2D subjects treated with diet and lifestyle changes as compared with nondiabetic subjects, changes that were often reversed by metformin. Our findings provide direct evidence that metformin monotherapy alters the human plasma lipidome independent of T2D disease control and support a potential cardioprotective effect of metformin worthy of future study. SIGNIFICANCE STATEMENT: This work provides important new information on the systemic effects of metformin in type 2 diabetic subjects. We observed significant changes in the plasma lipidome with metformin therapy, with metabolite classes previously associated with cardiovascular disease risk significantly reduced as compared to diet and lifestyle changes. While cardiovascular disease risk was not a primary outcome of our study, our results provide a jumping-off point for future work into the cardioprotective effects of metformin, even in well-controlled type 2 diabetes.

Dirichlet process mixture models for the analysis of repeated attempt designs.

In longitudinal studies, it is not uncommon to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful provides useful information for the purposes of assessing missing data assumptions. This is because measurements from subjects who provide the data after numerous failed attempts may differ from those who provide the measurement after fewer attempts. Previous models for these designs were parametric and/or did not allow sensitivity analysis. For the former, there are always concerns about model misspecification and for the latter, sensitivity analysis is essential when conducting inference in the presence of missing data. Here, we propose a new approach which minimizes issues with model misspecification by using Bayesian nonparametrics for the observed data distribution. We also introduce a novel approach for identification and sensitivity analysis. We re-analyze the repeated attempts data from a clinical trial involving patients with severe mental illness and conduct simulations to better understand the properties of our approach.

Joint modeling the frequency and duration of accelerometer-measured physical activity from a lifestyle intervention trial.

Physical activity (PA) guidelines recommend that PA be accumulated in bouts of 10 minutes or more in duration. Recently, researchers have sought to better understand how participants in PA interventions increase their activity. Participants can increase their daily PA by increasing the number of PA bouts per day while keeping the duration of the bouts constant; they can keep the number of bouts constant but increase the duration of each bout; or participants can increase both the number of bouts and their duration. We propose a novel joint modeling framework for modeling PA bouts and their duration over time. Our joint model is comprised of two sub-models: a mixed-effects Poisson hurdle sub-model for the number of bouts per day and a mixed-effects location scale gamma regression sub-model to characterize the duration of the bouts and their variance. The model allows us to estimate how daily PA bouts and their duration vary together over the course of an intervention and by treatment condition and is specifically designed to capture the unique distributional features of bouted PA as measured by accelerometer: frequent measurements, zero-inflated bouts, and skewed bout durations. We apply our methods to the Make Better Choices study, a longitudinal lifestyle intervention trial to increase PA. We perform a simulation study to evaluate how well our model is able to estimate relationships between outcomes.

Random forest modelling demonstrates microglial and protein misfolding features to be key phenotypic markers in C9orf72-ALS.

Clinical heterogeneity observed across patients with amyotrophic lateral sclerosis (ALS) is a known complicating factor in identifying potential therapeutics, even within cohorts with the same mutation, such as C9orf72 hexanucleotide repeat expansions (HREs). Thus, further understanding of pathways underlying this heterogeneity is essential for appropriate ALS trial stratification and the meaningful assessment of clinical outcomes. It has been shown that both inflammation and protein misfolding can influence ALS pathogenesis, such as the manifestation or severity of motor or cognitive symptoms. However, there has yet to be a systematic and quantitative assessment of immunohistochemical markers to interrogate the potential relevance of these pathways in an unbiased manner. To investigate this, we extensively characterised features of commonly used glial activation and protein misfolding stains in thousands of images of post-mortem tissue from a heterogeneous cohort of deeply clinically profiled patients with a C9orf72 HRE. Using a random forest model, we show that microglial staining features are the most accurate classifiers of disease status in our panel and that clinicopathological relationships exist between microglial activation status, TDP-43 pathology, and language dysfunction. Furthermore, we detected spatially resolved changes in fused in sarcoma (FUS) staining, suggesting that liquid-liquid phase shift of this aggregation-prone RNA-binding protein may be important in ALS caused by a C9orf72 HRE. Interestingly, no one feature alone significantly impacted the predictiveness of the model, indicating that the collective examination of all features, or a combination of several features, is what allows the model to be predictive. Our findings provide further support to the hypothesis of dysfunctional immune regulation and proteostasis in the pathogenesis of C9-ALS and provide a framework for digital analysis of commonly used neuropathological stains as a tool to enrich our understanding of clinicopathological relationships within and between cohorts. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

ß-Cryptoxanthin Attenuates Cigarette-Smoke-Induced Lung Lesions in the Absence of Carotenoid Cleavage Enzymes (BCO1/BCO2) in Mice.

High dietary intake of ß-cryptoxanthin (BCX, an oxygenated provitamin A carotenoid) is associated with a lower risk of lung disease in smokers. BCX can be cleaved by ß-carotene-15,15'-oxygenase (BCO1) and ß-carotene-9',10'-oxygenase (BCO2) to produce retinol and apo-10'-carotenoids. We investigated whether BCX has protective effects against cigarette smoke (CS)-induced lung injury, dependent or independent of BCO1/BCO2 and their metabolites. Both BCO1-/-/BCO2-/- double knockout mice (DKO) and wild type (WT) littermates were supplemented with BCX 14 days and then exposed to CS for an additional 14 days. CS exposure significantly induced macrophage and neutrophil infiltration in the lung tissues of mice, regardless of genotypes, compared to the non-exposed littermates. BCX treatment significantly inhibited CS-induced inflammatory cell infiltration, hyperplasia in the bronchial epithelium, and enlarged alveolar airspaces in both WT and DKO mice, regardless of sex. The protective effects of BCX were associated with lower expression of IL-6, TNF-α, and matrix metalloproteinases-2 and -9. BCX treatment led to a significant increase in hepatic BCX levels in DKO mice, but not in WT mice, which had significant increase in hepatic retinol concentration. No apo-10'-carotenoids were detected in any of the groups. In vitro BCX, at comparable doses of 3-OH-ß-apo-10'-carotenal, was effective at inhibiting the lipopolysaccharide-induced inflammatory response in a human bronchial epithelial cell line. These data indicate that BCX can serve as an effective protective agent against CS-induced lung lesions in the absence of carotenoid cleavage enzymes.