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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.

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 http://www.rampsearch.com.. Examples are presented to demonstrate the utility of RAMP for pathway-based searches of molecular imaging probes. [ABSTRACT FROM AUTHOR]

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., (JCOPDF © 2024.)

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.

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 FEV 1 (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 FEV 1 . 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.