Background
In chronic cystic fibrosis (CF) lung disease, neutrophilic inflammation and T-cell inhibition occur concomitantly, partly due to neutrophil-mediated release of the T-cell inhibitory enzyme Arg1. However, the onset of this tonic inhibition of T cells, and the impact of pulmonary exacerbations (PEs) on this process, remain unknown.
Methods
Children with CF aged 0-5 years were enrolled in a longitudinal, single-center cohort study. Blood (n = 35) and bronchoalveolar lavage (BAL) fluid (n = 18) were collected at stable outpatient clinic visits or inpatient PE hospitalizations and analyzed by flow cytometry (for immune cell presence and phenotype) and 20-plex chemiluminescence assay (for immune mediators). Patients were categorized by PE history into (i) no prior PE, (ii) past history of PE prior to stable visit, or (iii) current PE.
Results
PEs were associated with increased concentration of both pro- and anti-inflammatory mediators in BAL, and increased neutrophil frequency and G-CSF in circulation. PE BAL samples showed a trend toward an increased frequency of hyperexocytic “GRIM” neutrophils, which we previously identified in chronic CF. Interestingly, expression levels of the T-cell receptor associated molecule CD3 and of the inhibitory programmed death-1 (PD-1) receptor were respectively decreased and increased on T cells from BAL compared to blood in all patients. When categorized by PE status, CD3 and PD-1 expression on blood T cells did not differ among patients, while CD3 expression was decreased, and PD-1 expression was increased on BAL T cells from patients with current PE.
Conclusions
Our findings suggest that airway T cells are engaged during early-life PEs, prior to the onset of chronic neutrophilic inflammation in CF. In addition, increased blood neutrophil frequency and a trend toward increased BAL frequency of hyperexocytic neutrophils suggest that childhood PEs may progressively shift the balance of CF airway immunity towards neutrophil dominance.
Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically prespecified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high dimensional setting. We employ adaptive L1 penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quantile levels to avoid some of the potential problems with model selection at individual quantile levels. Our proposed approach achieves consistent shrinkage of regression quantile estimates across a continuous range of quantiles levels, enhancing the flexibility and robustness of the existing penalized quantile regression methods. Our theoretical results include the oracle rate of uniform convergence and weak convergence of the parameter estimators. We also use numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposal.
Given the importance of identifying prodromes of dementia with specific etiologies, we assessed whether seven latent classes of mild cognitive impairment (MCI), defined empirically based on cognitive, functional, and neuropsychiatric information at initial visit, are associated with distinct clinical outcomes and neuropathological features. We separated 6034 participants with a baseline diagnosis of MCI into seven latent classes using previously defined criteria. We found that these latent classes of MCI differed significantly in their clinical outcomes, survival time, and neuropathology. Two amnestic multi-domain subgroups, as well as two other subgroups with functional impairments and neuropsychiatric disturbances, were at higher risk of not only a ‘pure’ form of Alzheimer's disease (AD) pathology, but also a ‘mixed’ pathology consisting of both AD and vascular features. Moreover, the seven latent classes had different risks of Lewy bodies, hippocampal sclerosis, and frontotemporal lobar degeneration (FTLD). This study indicates that data-driven subgroups of MCI are clinicopathologically informative and, with refinement, could lead to targeted interventions focused on each etiology.
Recurrent events data are frequently encountered in biomedical follow-up studies. The generalized accelerated recurrence time (GART) model (Sun et al., 2016), which formulates covariate effects on the time scale of the mean function of recurrent events (i.e., time to expected frequency), has arisen as a useful secondary analysis tool to provide meaningful physical interpretations. In this article, we investigate the GART model in a multivariate recurrent events setting, where subjects may experience multiple types of recurrent events and some event types may be missing. We propose methods for the GART model that utilize the inverse probability weighting technique or the estimating equation projection strategy to handle event types that are missing at random. The new methods do not require imposing any parametric model for the missing mechanism, and thus are robust; moreover, they enjoy easy and stable implementation. We establish the uniform consistency and weak convergence of the resulting estimators and develop appropriate inferential procedures. Extensive simulation studies and an application to a dataset from Cystic Fibrosis Foundation Patient Registry (CFFPR) illustrate the validity and practical utility of the proposed methods.
Cystic fibrosis (CF) lung disease progressively worsens from infancy to adulthood. Disease-driven changes in early CF airway fluid metabolites may identify therapeutic targets to curb progression. CF patients aged 12–38 months (n=24; three out of 24 later denoted as CF screen positive, inconclusive diagnosis) received chest computed tomography scans, scored by the Perth–Rotterdam Annotated Grid Morphometric Analysis for CF (PRAGMA-CF) method to quantify total lung disease (PRAGMA-%Dis) and components such as bronchiectasis (PRAGMA-%Bx). Small molecules in bronchoalveolar lavage fluid (BALF) were measured with high-resolution accurate-mass metabolomics. Myeloperoxidase (MPO) was quantified by ELISA and activity assays. Increased PRAGMA-%Dis was driven by bronchiectasis and correlated with airway neutrophils. PRAGMA-%Dis correlated with 104 metabolomic features (p<0.05, q<0.25). The most significant annotated feature was methionine sulfoxide (MetO), a product of methionine oxidation by MPO-derived oxidants. We confirmed the identity of MetO in BALF and used reference calibration to confirm correlation with PRAGMA-%Dis (Spearman’s ρ=0.582, p=0.0029), extending to bronchiectasis (PRAGMA-%Bx; ρ=0.698, p=1.5×10−4), airway neutrophils (ρ=0.569, p=0.0046) and BALF MPO (ρ=0.803, p=3.9×10−6). BALF MetO associates with structural lung damage, airway neutrophils and MPO in early CF. Further studies are needed to establish whether methionine oxidation directly contributes to early CF lung disease and explore potential therapeutic targets indicated by these findings.
Quantile regression has demonstrated promising utility in longitudinal data analysis. Existing work is primarily focused on modeling cross-sectional outcomes, while outcome trajectories often carry more substantive information in practice. In this work, we develop a trajectory quantile regression framework that is designed to robustly and flexibly investigate how latent individual trajectory features are related to observed subject characteristics. The proposed models are built under multilevel modeling with usual parametric assumptions lifted or relaxed. We derive our estimation procedure by novelly transforming the problem at hand to quantile regression with perturbed responses and adapting the bias correction technique for handling covariate measurement errors. We establish desirable asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulation studies confirm the validity of the proposed method as well as its robustness. An application to the DURABLE trial uncovers sensible scientific findings and illustrates the practical value of our proposals.
In practice, disease outcomes are often measured in a continuous scale, and classification of subjects into meaningful disease categories is of substantive interest. To address this problem, we propose a general analytic framework for determining cut-points of the continuous scale. We develop a unified approach to assessing optimal cut-points based on various criteria, including common agreement and association measures. We study the nonparametric estimation of optimal cut-points. Our investigation reveals that the proposed estimator, though it has been ad-hocly used in practice, pertains to nonstandard asymptotic theory and warrants modifications to traditional inferential procedures. The techniques developed in this work are generally adaptable to study other estimators that are maximizers of nonsmooth objective functions while not belonging to the paradigm of M-estimation. We conduct extensive simulations to evaluate the proposed method and confirm the derived theoretical results. The new method is illustrated by an application to a mental health study.
In many observational longitudinal studies, the outcome of interest presents a skewed distribution, is subject to censoring due to detection limit or other reasons, and is observed at irregular times that may follow a outcome-dependent pattern. In this work, we consider quantile regression modeling of such longitudinal data, because quantile regression is generally robust in handling skewed and censored outcomes and is flexible to accommodate dynamic covariate-outcome relationships. Specifically, we study a longitudinal quantile regression model that specifies covariate effects on the marginal quantiles of the longitudinal outcome. Such a model is easy to interpret and can accommodate dynamic outcome profile changes over time. We propose estimation and inference procedures that can appropriately account for censoring and irregular outcome-dependent follow-up. Our proposals can be readily implemented based on existing software for quantile regression. We establish the asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulations suggest good finite-sample performance of the new method. We also present an analysis of data from a long-term study of a population exposed to polybrominated biphenyls (PBB), which uncovers an inhomogeneous PBB elimination pattern that would not be detected by traditional longitudinal data analysis.
Aims: To determine the frequency of increasing levels of stress hyperglycemia and its associated complications in surgery patients without a history of diabetes. Methods: We reviewed hospital outcomes in 1971 general surgery patients with documented preoperative normoglycemia [blood glucose (BG) < 140 mg/dL] who developed stress hyperglycemia (BG > 140 mg/dL or > 180 mg/dL) within 48 h after surgery between 1/1/2010 and 10/31/2015. Results: A total of 415 patients (21%) had ≥ 1 episode of BG between 140 and 180 mg/dL and 206 patients (10.5%) had BG > 180 mg/dL. The median length of hospital stay (LOS) was 9 days [interquartile range (IQR) 5,15] for BG between 140 and 180 mg/dL and 12 days (IQR 6,18) for BG > 180 mg/dL compared to normoglycemia at 6 days (IQR 4,11), both p < 0.001. Patients with BG 140–180 mg/dL had higher rates of complications with an odds ratio (OR) of 1.68 [95% confidence interval (95% CI) 1.15–2.44], and those with BG > 180 mg/dL had more complications [OR 3.46 (95% CI 2.24–5.36)] and higher mortality [OR 6.56 (95% CI 2.12–20.27)] compared to normoglycemia. Conclusion: Increasing levels of stress hyperglycemia are associated with higher rates of perioperative complications and hospital mortality in surgical patients without diabetes.
Objectives Examine evidence for different subclasses of posttraumatic stress disorder (PTSD) symptoms in a sample of trauma exposed, low-income, predominantly African American men and women. Assess the relationship between PTSD subclasses with major depressive disorder (MDD) and types of trauma experienced. Method Latent class analysis (LCA) using a multivariate normal mixture model on the 17-item PTSD Symptom Scale (PSS) was used to identify latent subclasses of PTSD symptoms (N = 5063). Results LCA suggested four subclasses of PTSD symptoms: (1) High severity and comorbidity (n = 932, 92.2% current PTSD, 88.7% MDD, 82% both), characterized by high PTSD symptoms, depression, and comorbidity of PTSD and MDD; (2) Moderate severity (n = 1179, 56.5% current PTSD, 53.9% MDD, 34.5% both), which had high avoidance and hyper-vigilance symptoms compared to the other symptoms; (3) Low PTSD and high depression (n = 657, 12.8% current PTSD, 49.9% MDD, 8.8% both) which had high insomnia but otherwise low PTSD symptoms and high depression; and (4) Resilient (n = 2295, 2.0% current PTSD, 16.4% MDD, and 0.6% both) characterized by low mean scores on all PTSD symptoms and depression. Conclusions The results suggest avoidance and hyper-vigilance are important symptoms in PTSD development and insomnia may be an important indicator for depression. The combination of severe insomnia, avoidance, and hyper-vigilance may be key symptoms for comorbidity of PTSD and MDD. Future studies should focus on these symptoms to better target people at high risk for developing PTSD or MDD.