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- McWhinney, Sean R, et al.
(författare)
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Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.
- 2024
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Ingår i: Human brain mapping. - 1097-0193. ; 45:8
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.
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- Hebestreit, H., et al.
(författare)
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Cardiopulmonary exercise testing provides additional prognostic information in cystic fibrosis
- 2019
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Ingår i: American Journal of Respiratory and Critical Care Medicine. - 1073-449X. ; 199:8, s. 987-995
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Tidskriftsartikel (refereegranskat)abstract
- Rationale: The prognostic value of cardiopulmonary exercise testing (CPET) for survival in cystic fibrosis (CF) in the context of current clinical management, when controlling for other known prognostic factors, is unclear. Objectives: To determine the prognostic value of CPET-derived measures beyond peak oxygen uptake (VO 2 peak) following rigorous adjustment for other predictors. Methods: Data from 10 CF centers in Australia, Europe, and North America were collected retrospectively. A total of 510 patients completed a cycle CPET between January 2000 and December 2007, of which 433 fulfilled the criteria for a maximal effort. Time to death/lung transplantation was analyzed using Cox proportional hazards regression. In addition, phenotyping using hierarchical Ward clustering was performed to characterize high-risk subgroups. Measurements and Main Results: Cox regression showed, even after adjustment for sex, FEV1% predicted, body mass index (z-score), age at CPET, Pseudomonas aeruginosa status, and CF-related diabetes as covariates in the model, that VO 2 peak in % predicted (hazard ratio [HR], 0.964; 95% confidence interval [CI], 0.944-0.986), peak work rate (% predicted; HR, 0.969; 95% CI, 0.951-0.988), ventilatory equivalent for oxygen (HR, 1.085; 95% CI, 1.041-1.132), and carbon dioxide (HR, 1.060; 95% CI, 1.007-1.115) (all P < 0.05) were significant predictors of death or lung transplantation at 10-year follow-up. Phenotyping revealed that CPET-derived measures were important for clustering. We identified a high-risk cluster characterized by poor lung function, nutritional status, and exercise capacity. Conclusions: CPET provides additional prognostic information to established predictors of death/lung transplantation in CF. High-risk patients may especially benefit from regular monitoring of exercise capacity and exercise counseling. Copyright © 2019 by the American Thoracic Society.
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- van Meel, Evelien R., et al.
(författare)
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Early-life respiratory tract infections and the risk of school-age lower lung function and asthma: a meta-analysis of 150 000 European children
- 2022
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Ingår i: European Respiratory Journal. - : EUROPEAN RESPIRATORY SOC JOURNALS LTD. - 0903-1936 .- 1399-3003. ; 60:4
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Tidskriftsartikel (refereegranskat)abstract
- Background Early-life respiratory tract infections might affect chronic obstructive respiratory diseases, but conclusive studies from general populations are lacking. Our objective was to examine if children with early-life respiratory tract infections had increased risks of lower lung function and asthma at school age. Methods We used individual participant data of 150 090 children primarily from the EU Child Cohort Network to examine the associations of upper and lower respiratory tract infections from age 6 months to 5 years with forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC, forced expiratory flow at 75% of FVC (FEF75%) and asthma at a median (range) age of 7 (4-15) years. Results Children with early-life lower, not upper, respiratory tract infections had a lower school-age FEV1, FEV1/FVC and FEF75% (z-score range: -0.09 (95% CI -0.14- -0.04) to -0.30 (95% CI -0.36- -0.24)). Children with early-life lower respiratory tract infections had a higher increased risk of school-age asthma than those with upper respiratory tract infections (OR range: 2.10 (95% CI 1.98-2.22) to 6.30 (95% CI 5.64-7.04) and 1.25 (95% CI 1.18-1.32) to 1.55 (95% CI 1.47-1.65), respectively). Adjustment for preceding respiratory tract infections slightly decreased the strength of the effects. Observed associations were similar for those with and without early-life wheezing as a proxy for early-life asthma. Conclusions Our findings suggest that early-life respiratory tract infections affect development of chronic obstructive respiratory diseases in later life, with the strongest effects for lower respiratory tract infections.
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