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Rationale: Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition. Objectives: We hypothesized that the unbiased integration of different COPD lung omics using a novel multilayer approach might unravel mechanisms associated with clinical characteristics. Methods: We profiled mRNA, microRNA and methylome in lung tissue samples from 135 former smokers with COPD. For each omic (layer), we built a patient network on the basis of molecular similarity. The three networks were used to build a multilayer network, and optimization of multiplex modularity was used to identify patient communities across the three distinct layers. Uncovered communities were related to clinical features. Measurements and Main Results: We identified five patient communities in the multilayer network that were molecularly distinct and related to clinical characteristics, such as FEV 1 and blood eosinophils. Two communities (C#3 and C#4) had both similarly low FEV 1 values and emphysema but were molecularly different: C#3, but not C#4, presented B- and T-cell signatures and a downregulation of secretory (SCGB1A1/SCGB3A1) and ciliated cells. A machine learning model was set up to discriminate C#3 and C#4 in our cohort and to validate them in an independent cohort. Finally, using spatial transcriptomics, we characterized the small airway differences between C#3 and C#4, identifying an upregulation of T-/B-cell homing chemokines and bacterial response genes in C#3. Conclusions: A novel multilayer network analysis is able to identify clinically relevant COPD patient communities. Patients with similarly low FEV 1 and emphysema can have molecularly distinct small airways and immune response patterns, indicating that different endotypes can lead to similar clinical presentation.