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Chronic Obstructive Pulmonary Disease heterogeneity: challenges for health risk assessment, stratification and management.

Roca J, Vargas C, Cano I, Selivanov V, Barreiro E, Maier D, Falciani F, Wagner P, Cascante M, Garcia-Aymerich J, Kalko S, De Mas I, Tegnér J, Escarrabill J, Agustí A, Gomez-Cabrero D, Synergy-COPD consorti - J Transl Med (2014)

Bottom Line: To this end, strategies combining deterministic modeling and network medicine analyses of the Biobridge dataset were used to investigate the mechanisms of skeletal muscle dysfunction.An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was performed using a large dataset (ICD9-CM data from Medicare, 13 million people).Finally, a targeted network analysis using the outcomes of the two approaches (skeletal muscle dysfunction and co-morbidity clustering) explored shared pathways between these phenomena. (1) Evidence of abnormal regulation of skeletal muscle bioenergetics and skeletal muscle remodeling showing a significant association with nitroso-redox disequilibrium was observed in COPD; (2) COPD patients presented higher risk for co-morbidity clustering than non-COPD patients increasing with ageing; and, (3) the on-going targeted network analyses suggests shared pathways between skeletal muscle dysfunction and co-morbidity clustering.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background and hypothesis: Heterogeneity in clinical manifestations and disease progression in Chronic Obstructive Pulmonary Disease (COPD) lead to consequences for patient health risk assessment, stratification and management. Implicit with the classical "spill over" hypothesis is that COPD heterogeneity is driven by the pulmonary events of the disease. Alternatively, we hypothesized that COPD heterogeneities result from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering, each of them with their own dynamics.

Objective and method: To explore the potential of a systems analysis of COPD heterogeneity focused on skeletal muscle dysfunction and on co-morbidity clustering aiming at generating predictive modeling with impact on patient management. To this end, strategies combining deterministic modeling and network medicine analyses of the Biobridge dataset were used to investigate the mechanisms of skeletal muscle dysfunction. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was performed using a large dataset (ICD9-CM data from Medicare, 13 million people). Finally, a targeted network analysis using the outcomes of the two approaches (skeletal muscle dysfunction and co-morbidity clustering) explored shared pathways between these phenomena.

Results: (1) Evidence of abnormal regulation of skeletal muscle bioenergetics and skeletal muscle remodeling showing a significant association with nitroso-redox disequilibrium was observed in COPD; (2) COPD patients presented higher risk for co-morbidity clustering than non-COPD patients increasing with ageing; and, (3) the on-going targeted network analyses suggests shared pathways between skeletal muscle dysfunction and co-morbidity clustering.

Conclusions: The results indicate the high potential of a systems approach to address COPD heterogeneity. Significant knowledge gaps were identified that are relevant to shape strategies aiming at fostering 4P Medicine for patients with COPD.

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Metabolic analysis. Upper panel: Resting individual metabolic profiles in COPD patients (spheres) and in healthy sedentary subjects (cubes), including pre (black symbols) - and post -training data (grey symbols). The results are expressed by the three Latent Variables (LV1, 2 and 3) of the partial-least square discriminant analysis (PLS-DA). The percentages indicate the magnitude of the differences between the two groups of subjects for each dimension (p<0.05). Bottom panel: Endurance training responses of individual metabolites. Mean training-induced responses of individual metabolites. Data expressed as percent of change are indicated as mean ± SEM. (*p<0.001; † p<0.01; ‡ p<0.05) (reproduced from [27]with permission)
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Figure 5: Metabolic analysis. Upper panel: Resting individual metabolic profiles in COPD patients (spheres) and in healthy sedentary subjects (cubes), including pre (black symbols) - and post -training data (grey symbols). The results are expressed by the three Latent Variables (LV1, 2 and 3) of the partial-least square discriminant analysis (PLS-DA). The percentages indicate the magnitude of the differences between the two groups of subjects for each dimension (p<0.05). Bottom panel: Endurance training responses of individual metabolites. Mean training-induced responses of individual metabolites. Data expressed as percent of change are indicated as mean ± SEM. (*p<0.001; † p<0.01; ‡ p<0.05) (reproduced from [27]with permission)

Mentions: The Biobridge dataset [27,28,33] clearly indicated that COPD patients at rest, before training, showed nitroso-redox disequilibrium both in blood and skeletal muscle compared to healthy controls (Figure 4, upper panel). Moreover, a significant association of protein carbonylation levels between blood and skeletal muscle was observed in the patients (Figure 4, bottom panel) [28] in whom low-grade inflammation in peripheral blood, but not in skeletal muscle, was observed. The plasma metabolomic analysis in COPD was fully consistent with abnormal skeletal muscle abnormalities reported in these patients [27], namely: decreased oxidative capacity leading to abnormal ROS generation [28,44,45], up-regulation of glycolysis [46] and altered aminoacid metabolism [27,46] (Figure 5). The transcriptomic analysis showed lower and abnormal skeletal muscle gene expression at baseline in COPD patients compared to healthy subjects with clear differences between COPD patients with preserved skeletal muscle mass and those showing muscle wasting [33].


Chronic Obstructive Pulmonary Disease heterogeneity: challenges for health risk assessment, stratification and management.

Roca J, Vargas C, Cano I, Selivanov V, Barreiro E, Maier D, Falciani F, Wagner P, Cascante M, Garcia-Aymerich J, Kalko S, De Mas I, Tegnér J, Escarrabill J, Agustí A, Gomez-Cabrero D, Synergy-COPD consorti - J Transl Med (2014)

Metabolic analysis. Upper panel: Resting individual metabolic profiles in COPD patients (spheres) and in healthy sedentary subjects (cubes), including pre (black symbols) - and post -training data (grey symbols). The results are expressed by the three Latent Variables (LV1, 2 and 3) of the partial-least square discriminant analysis (PLS-DA). The percentages indicate the magnitude of the differences between the two groups of subjects for each dimension (p<0.05). Bottom panel: Endurance training responses of individual metabolites. Mean training-induced responses of individual metabolites. Data expressed as percent of change are indicated as mean ± SEM. (*p<0.001; † p<0.01; ‡ p<0.05) (reproduced from [27]with permission)
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4255905&req=5

Figure 5: Metabolic analysis. Upper panel: Resting individual metabolic profiles in COPD patients (spheres) and in healthy sedentary subjects (cubes), including pre (black symbols) - and post -training data (grey symbols). The results are expressed by the three Latent Variables (LV1, 2 and 3) of the partial-least square discriminant analysis (PLS-DA). The percentages indicate the magnitude of the differences between the two groups of subjects for each dimension (p<0.05). Bottom panel: Endurance training responses of individual metabolites. Mean training-induced responses of individual metabolites. Data expressed as percent of change are indicated as mean ± SEM. (*p<0.001; † p<0.01; ‡ p<0.05) (reproduced from [27]with permission)
Mentions: The Biobridge dataset [27,28,33] clearly indicated that COPD patients at rest, before training, showed nitroso-redox disequilibrium both in blood and skeletal muscle compared to healthy controls (Figure 4, upper panel). Moreover, a significant association of protein carbonylation levels between blood and skeletal muscle was observed in the patients (Figure 4, bottom panel) [28] in whom low-grade inflammation in peripheral blood, but not in skeletal muscle, was observed. The plasma metabolomic analysis in COPD was fully consistent with abnormal skeletal muscle abnormalities reported in these patients [27], namely: decreased oxidative capacity leading to abnormal ROS generation [28,44,45], up-regulation of glycolysis [46] and altered aminoacid metabolism [27,46] (Figure 5). The transcriptomic analysis showed lower and abnormal skeletal muscle gene expression at baseline in COPD patients compared to healthy subjects with clear differences between COPD patients with preserved skeletal muscle mass and those showing muscle wasting [33].

Bottom Line: To this end, strategies combining deterministic modeling and network medicine analyses of the Biobridge dataset were used to investigate the mechanisms of skeletal muscle dysfunction.An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was performed using a large dataset (ICD9-CM data from Medicare, 13 million people).Finally, a targeted network analysis using the outcomes of the two approaches (skeletal muscle dysfunction and co-morbidity clustering) explored shared pathways between these phenomena. (1) Evidence of abnormal regulation of skeletal muscle bioenergetics and skeletal muscle remodeling showing a significant association with nitroso-redox disequilibrium was observed in COPD; (2) COPD patients presented higher risk for co-morbidity clustering than non-COPD patients increasing with ageing; and, (3) the on-going targeted network analyses suggests shared pathways between skeletal muscle dysfunction and co-morbidity clustering.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background and hypothesis: Heterogeneity in clinical manifestations and disease progression in Chronic Obstructive Pulmonary Disease (COPD) lead to consequences for patient health risk assessment, stratification and management. Implicit with the classical "spill over" hypothesis is that COPD heterogeneity is driven by the pulmonary events of the disease. Alternatively, we hypothesized that COPD heterogeneities result from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering, each of them with their own dynamics.

Objective and method: To explore the potential of a systems analysis of COPD heterogeneity focused on skeletal muscle dysfunction and on co-morbidity clustering aiming at generating predictive modeling with impact on patient management. To this end, strategies combining deterministic modeling and network medicine analyses of the Biobridge dataset were used to investigate the mechanisms of skeletal muscle dysfunction. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was performed using a large dataset (ICD9-CM data from Medicare, 13 million people). Finally, a targeted network analysis using the outcomes of the two approaches (skeletal muscle dysfunction and co-morbidity clustering) explored shared pathways between these phenomena.

Results: (1) Evidence of abnormal regulation of skeletal muscle bioenergetics and skeletal muscle remodeling showing a significant association with nitroso-redox disequilibrium was observed in COPD; (2) COPD patients presented higher risk for co-morbidity clustering than non-COPD patients increasing with ageing; and, (3) the on-going targeted network analyses suggests shared pathways between skeletal muscle dysfunction and co-morbidity clustering.

Conclusions: The results indicate the high potential of a systems approach to address COPD heterogeneity. Significant knowledge gaps were identified that are relevant to shape strategies aiming at fostering 4P Medicine for patients with COPD.

Show MeSH
Related in: MedlinePlus