Temporal Exploration of COPD Phenotypes: Insights from the COPDGene and SPIROMICS Cohorts.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) exhibits considerable progression heterogeneity. We hypothesized that elastic principal graph analysis (EPGA) would identify distinct clinical phenotypes and their longitudinal relationships. METHODS: Cross-sectional data from 8,972 tobacco-exposed COPDGene participants, with and without COPD, were used to train a model with EPGA, using thirty clinical, physiologic and CT features. Principal component analysis (PCA) was used to reduce data dimensionality to six principal components. An elastic principal tree was fitted to the reduced space. 4,585 participants from COPDGene Phase 2 were used to test longitudinal trajectories. 2,652 participants from SPIROMICS tested external reproducibility. RESULTS: Our analysis used cross-sectional data to create an elastic principal tree, where the concept of time is represented by distance on the tree. Six clinically distinct tree segments were identified that differed by lung function, symptoms, and CT features: 1) Subclinical (SC); 2) Parenchymal Abnormality (PA); 3) Chronic Bronchitis (CB); 4) Emphysema Male (EM); 5) Emphysema Female (EF); and 6) Severe Airways (SA) disease. Cross-sectional SPIROMICS data confirmed similar groupings. 5-year data from COPDGene mapped longitudinal changes onto the tree. 29% of patients changed segment during follow-up; longitudinal trajectories confirmed a net flow of patients along the tree, from SC towards Emphysema, although alternative trajectories were noted, through airway disease predominant phenotypes, CB and SA. CONCLUSION: This novel analytic methodology provides an approach to defining longitudinal phenotypic trajectories using cross sectional data. These insights are clinically relevant and could facilitate precision therapy and future trials to modify disease progression.

Multimorbidities in COPD are Associated With Increased Exacerbations and Health Care Resource Utilization in Real-World Patients from a U.S. Database.

Background: Patients with chronic obstructive pulmonary disease (COPD) often develop other morbidities, suggesting a systemic component to this disease. This retrospective noninterventional cohort study investigated relationships between multimorbidities in COPD and their impact on COPD exacerbations and COPD-related health care resource utilization (HCRU) using real-world evidence from Optum's de-identified Clinformatics® Data Mart Database. Methods: Demographic and clinical characteristics were assessed. Overall comorbidity burden and proportion of individuals with gastroesophageal reflux disease (GERD), diabetes, or osteoporosis/osteopenia were compared in age-matched COPD versus non-COPD cohorts using descriptive statistics. COPD exacerbations and COPD-related HCRU (hospitalizations and emergency department visits) were compared between age-matched cohorts of COPD patients with and without specific common morbidities (GERD, diabetes, and osteoporosis/osteopenia). Additional weight-matching was performed for matched cohorts of COPD patients with and without diabetes, and with and without osteoporosis/osteopenia. The follow-up period was 5 years. Results: Age-matched cohorts with and without COPD each comprised 158,106 patients. Morbidities were more common in the COPD cohort than the cohort without COPD (GERD: 44.9% versus 27.8%; diabetes: 40.8% versus 31.1%; osteoporosis/osteopenia: 18.8% versus 14.1%, respectively). Compared with matched cohorts with COPD only, cohorts of COPD patients with either GERD, diabetes, or osteoporosis/osteopenia experienced increased risk of severe exacerbations (odds ratio [OR]=1.819, OR=1.119, and OR=1.373, respectively), moderate exacerbations (OR=1.699, OR=1.102, and OR=1.322, respectively), or any exacerbations (OR=1.848, OR=1.099, and OR=1.384, respectively, p<0.001 for all comparisons) and increased risk of COPD-related HCRU (emergency department visits: OR=1.983, OR=1.098, and OR=1.343, respectively; hospitalization visits: OR=2.222, OR=1.26, and OR=1.368, respectively; p<0.001 for all comparisons). Conclusion: These real-world data confirm that GERD, diabetes, and osteoporosis are common morbidities in patients with COPD and, moreover, that they affect frequency of exacerbation and HCRU. Determining and addressing the mechanisms behind the systemic effects of COPD may be beneficial for COPD patients and may also help reduce COPD exacerbations.

Dynamic and prognostic proteomic associations with FEV1 decline in chronic obstructive pulmonary disease.

Rationale: Identification and validation of circulating biomarkers for lung function decline in COPD remains an unmet need. Objective: Identify prognostic and dynamic plasma protein biomarkers of COPD progression. Methods: We measured plasma proteins using SomaScan from two COPD-enriched cohorts, the Subpopulations and Intermediate Outcomes Measures in COPD Study (SPIROMICS) and Genetic Epidemiology of COPD (COPDGene), and one population-based cohort, Multi-Ethnic Study of Atherosclerosis (MESA) Lung. Using SPIROMICS as a discovery cohort, linear mixed models identified baseline proteins that predicted future change in FEV1 (prognostic model) and proteins whose expression changed with change in lung function (dynamic model). Findings were replicated in COPDGene and MESA-Lung. Using the COPD-enriched cohorts, Gene Set Enrichment Analysis (GSEA) identified proteins shared between COPDGene and SPIROMICS. Metascape identified significant associated pathways. Measurements and Main Results: The prognostic model found 7 significant proteins in common (p < 0.05) among all 3 cohorts. After applying false discovery rate (adjusted p < 0.2), leptin remained significant in all three cohorts and growth hormone receptor remained significant in the two COPD cohorts. Elevated baseline levels of leptin and growth hormone receptor were associated with slower rate of decline in FEV1. Twelve proteins were nominally but not FDR significant in the dynamic model and all were distinct from the prognostic model. Metascape identified several immune related pathways unique to prognostic and dynamic proteins. Conclusion: We identified leptin as the most reproducible COPD progression biomarker. The difference between prognostic and dynamic proteins suggests disease activity signatures may be different from prognosis signatures.

RAMP: a bioinformatics framework for researching imaging agents through molecular pathways.

Signaling pathways are the fundamental grammar of cellular communication, yet few frameworks are available to analyze molecular imaging probes in the context of signaling pathways. Such a framework would aid in the design and selection of imaging probes for measuring specific signaling pathways and, vice versa, help illuminate which pathways are being assayed by a given probe. RAMP (Researching imaging Agents through Molecular Pathways) is a bioinformatics framework for connecting signaling pathways and imaging probes using a controlled vocabulary of the imaging targets. RAMP contains signaling pathway data from MetaCore, the Kyoto Encyclopedia of Genes and Genomes, and the Gene Ontology project; imaging probe data from the Molecular Imaging and Contrast Agent Database (MICAD); and tissue protein expression data from The Human Protein Atlas. The RAMP search tool is available at . Examples are presented to demonstrate the utility of RAMP for pathway-based searches of molecular imaging probes.

Dietary Factors Impact Developmental Trajectories in Young Autistic Children.

PURPOSE: The purpose of this research was to investigate the impact of dietary factors on developmental trajectories in young autistic children. METHODS: A gluten-free and casein-free diets, as well as six types of food (meat and eggs, vegetables, uncooked vegetables, sweets, bread, and "white soft bread that never molds") were investigated observationally for up to three years in 5,553 children 2 to 5 years of age via parent-report measures completed within a mobile application. Children had a parent-reported diagnosis of Autism Spectrum Disorder (ASD); 78% were males; the majority of participants resided in the USA. Outcome was monitored on five orthogonal subscales: Language Comprehension, Expressive Language, Sociability, Sensory Awareness, and Health, assessed by the Autism Treatment Evaluation Checklist (ATEC) (Rimland & Edelson, 1999) and Mental Synthesis Evaluation Checklist (MSEC) (Arnold & Vyshedskiy, 2022; Braverman et al., 2018). RESULTS: Consumption of fast-acting carbohydrates - sweets, bread, and "white soft bread that never molds" - was associated with a significant and a consistent Health subscale score decline. On the contrary, a gluten-free diet, as well as consumption of meat, eggs, and vegetables were associated with a significant and consistent improvement in the Language Comprehension score. Consumption of meat and eggs was also associated with a significant and consistent improvement in the Sensory Awareness score. CONCLUSION: The results of this study demonstrate a strong correlation between a diet and developmental trajectories and suggest possible dietary interventions for young autistic children.

Watching Videos and Television Is Related to a Lower Development of Complex Language Comprehension in Young Children with Autism.

The effect of passive video and television watching duration on 2- to 5-year-old children with autism was investigated in the largest and the longest observational study to date. Parents assessed the development of 3227 children quarterly for three years. Longer video and television watching were associated with better development of expressive language but significantly impeded development of complex language comprehension. On an annualized basis, low TV users (low quartile: 40 min or less of videos and television per day) improved their language comprehension 1.4 times faster than high TV users (high quartile: 2 h or more of videos and television per day). This difference was statistically significant. At the same time, high TV users improved their expressive language 1.3 times faster than low TV users. This difference was not statistically significant. No effect of video and television watching duration on sociability, cognition, or health was detected.

Longitudinal Epidemiological Study of Autism Subgroups Using Autism Treatment Evaluation Checklist (ATEC) Score.

Here we report the results of the subgroup analyses of an observational cohort of children whose parents completed the Autism Treatment Evaluation Checklist (ATEC) over the period of several years. A linear mixed effects model was used to evaluate longitudinal changes in ATEC scores within different patient subgroups. All groups decreased their mean ATEC score over time indicating improvement of symptoms, however there were significant differences between the groups. Younger children improved more than the older children. Children with milder ASD improved more than children with more severe ASD in the Communication subscale. There was no difference in improvement between females vs. males. One surprising finding was that children from developed English-speaking countries improved less than children from non-English-speaking countries.

Novel Prefrontal Synthesis Intervention Improves Language in Children with Autism.

Prefrontal synthesis (PFS) is defined as the ability to juxtapose mental visuospatial objects at will. Paralysis of PFS may be responsible for the lack of comprehension of spatial prepositions, semantically-reversible sentences, and recursive sentences observed in 30 to 40% of individuals with autism spectrum disorder (ASD). In this report we present data from a three-year-long clinical trial of 6454 ASD children age 2 to 12 years, which were administered a PFS-targeting intervention. Tablet-based verbal and nonverbal exercises emphasizing mental-juxtaposition-of-objects were organized into an application called Mental Imagery Therapy for Autism (MITA). The test group included participants who completed more than one thousand exercises and made no more than one error per exercise. The control group was selected from the rest of participants by a matching procedure. Each test group participant was matched to the control group participant by age, gender, expressive language, receptive language, sociability, cognitive awareness, and health score at first evaluation using propensity score analysis. The test group showed a 2.2-fold improvement in receptive language score vs. control group (p < 0.0001) and a 1.4-fold improvement in expressive language (p = 0.0144). No statistically significant change was detected in other subscales not targeted by the exercises. These findings show that language acquisition improves after training PFS and that a further investigation of the PFS-targeting intervention in a randomized controlled study is warranted.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development.

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

The use of haplotype-specific transcripts improves sample annotation consistency.

BACKGROUND: Exact sample annotation in expression microarray datasets is essential for any type of pharmacogenomics research. RESULTS: Candidate markers were explored through the application of Hartigans' dip test statistics to a publically available human whole genome microarray dataset. The marker performance was tested on 188 serial samples from 53 donors and of variable tissue origin from five public microarray datasets. A qualified transcript marker panel consisting of three probe sets for human leukocyte antigens HLA-DQA1 (2 probe sets) and HLA-DRB4 identified sample donor identifier inconsistencies in six of the 188 test samples. About 3% of the test samples require root-cause analysis due to unresolvable inaccuracies. CONCLUSIONS: The transcript marker panel consisting of HLA-DQA1 and HLA-DRB4 represents a robust, tissue-independent composite marker to assist control donor annotation concordance at the transcript level. Allele-selectivity of HLA genes renders them good candidates for "fingerprinting" with donor specific expression pattern.