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. 2021 Dec;12(6):1803-1817.
doi: 10.1002/jcsm.12782. Epub 2021 Sep 15.

Genetic variation in genes regulating skeletal muscle regeneration and tissue remodelling associated with weight loss in chronic obstructive pulmonary disease

Affiliations

Genetic variation in genes regulating skeletal muscle regeneration and tissue remodelling associated with weight loss in chronic obstructive pulmonary disease

Preeti Lakshman Kumar et al. J Cachexia Sarcopenia Muscle. 2021 Dec.

Erratum in

Abstract

Background: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally. COPD patients with cachexia or weight loss have increased risk of death independent of body mass index (BMI) and lung function. We tested the hypothesis genetic variation is associated with weight loss in COPD using a genome-wide association study approach.

Methods: Participants with COPD (N = 4308) from three studies (COPDGene, ECLIPSE, and SPIROMICS) were analysed. Discovery analyses were performed in COPDGene with replication in SPIROMICS and ECLIPSE. In COPDGene, weight loss was defined as self-reported unintentional weight loss > 5% in the past year or low BMI (BMI < 20 kg/m2 ). In ECLIPSE and SPIROMICS, weight loss was calculated using available longitudinal visits. Stratified analyses were performed among African American (AA) and Non-Hispanic White (NHW) participants with COPD. Single variant and gene-based analyses were performed adjusting for confounders. Fine mapping was performed using a Bayesian approach integrating genetic association results with linkage disequilibrium and functional annotation. Significant gene networks were identified by integrating genetic regions associated with weight loss with skeletal muscle protein-protein interaction (PPI) data.

Results: At the single variant level, only the rs35368512 variant, intergenic to GRXCR1 and LINC02383, was associated with weight loss (odds ratio = 3.6, 95% confidence interval = 2.3-5.6, P = 3.2 × 10-8 ) among AA COPD participants in COPDGene. At the gene level in COPDGene, EFNA2 and BAIAP2 were significantly associated with weight loss in AA and NHW COPD participants, respectively. The EFNA2 association replicated among AA from SPIROMICS (P = 0.0014), whereas the BAIAP2 association replicated in NHW from ECLIPSE (P = 0.025). The EFNA2 gene encodes the membrane-bound protein ephrin-A2 involved in the regulation of developmental processes and adult tissue homeostasis such as skeletal muscle. The BAIAP2 gene encodes the insulin-responsive protein of mass 53 kD (IRSp53), a negative regulator of myogenic differentiation. Integration of the gene-based findings participants with PPI data revealed networks of genes involved in pathways such as Rho and synapse signalling.

Conclusions: The EFNA2 and BAIAP2 genes were significantly associated with weight loss in COPD participants. Collectively, the integrative network analyses indicated genetic variation associated with weight loss in COPD may influence skeletal muscle regeneration and tissue remodelling.

Keywords: Biomarkers; COPD; Cachexia; GWAS; Genetics; Skeletal muscle regeneration; Tissue remodelling; Weight loss.

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Conflict of interest statement

R.T‐S. is a former employee and current shareholder of GSK the sponsor of the ECLIPSE study. She declares personal fees from Immunomet, Vocalis Health, Ena Respiratory and Teva. E.K.S. has received institutional grant support from GSK and Bayer. M.W. has received institutional grant support from Mereo BioPharma, Grifols, Vertex Pharmaceuticals, Bayer AG, ARCUS‐Med, Verona, and has been in consultancy roles for AstraZeneca, GlaxoSmithKline, Boehringer Ingelheim, and Takeda. G.R.W. has received institutional grant support from Janssen Pharmaceuticals and Boehringer Ingelheim and has been in consultancy roles for Boehringer Ingelheim, CSL Behring, Janssen Pharmaceuticals, Novartis and Vertex. G.R.W. is also a founder and equity holder of Quantitative Imaging Solutions, a data and image analytics company. W.W.L. reports grants from NIH/NHLBI, non‐financial support from Pulmonx and personal fees from Konica Minolta. M.H.C. has received grant funding from Bayer and GSK and speaking or consulting fees from Genentech, AstraZeneca, and Illumina. H.B.R. received institutional grant support from GlaxoSmithKline, AstraZeneca, and Boehringer Ingelheim and consultancy fees from Boehringer Ingelheim, Astellas Pharma and Omniox Inc. M.T.D. reports grants from NIH, Department of Defense, and the American Lung Association; contracted clinical trial support from AstraZeneca, Boehringer Ingelheim, Boston Scientific, Gala, GlaxoSmithKline, Nuvaira, PneumRx/BTG, and Pulmonx; travel from Pulmonx; and consulting fees from AstraZeneca, GlaxoSmithKline, Mereo, Quark, and Teva.

Figures

Figure 1
Figure 1
(A) Regional association plot for top weight loss variant region in African American participants from COPDGene near variant, 4:43232667. (B) Regional association plot for top weight loss variant region in non‐Hispanic White COPD participants from COPDGene on chromosome 19. In both panels, lead SNPs denoted in purple. Colour of remaining SNPs indicates degree of linkage disequilibrium with the lead SNP, as measured by r 2, the squared coefficient of correlation.
Figure 2
Figure 2
Fine mapping of EFNA2 region associated with weight loss among African American COPD participants. (A) Scatterplot of location vs. posterior probabilities with credible set, (B) physical position of EFNA2, and (C) functional annotation tracks.
Figure 3
Figure 3
Fine mapping of BAIAP2 region associated with weight loss among non‐Hispanic White COPD participants. (A) Scatterplot of location vs. posterior probabilities with credible set, (B) physical position of genes including BAIAP2 in region, and (C) functional annotation tracks.
Figure 4
Figure 4
COPD weight loss consensus network generated from African American participant analyses. Node size is proportional to P value significance where the bigger the node size the smaller the P value from the gene‐based meta‐analysis result. Nodes (circles) represent genes that are among the top genes in the consensus network. Edges (lines) are known protein–protein (PPI) interactions. Nodes filled in black represent genes robust to exclusion of UBC from the PPI network.
Figure 5
Figure 5
COPD weight loss consensus network generated from non‐Hispanic White participant analyses. Node size is proportional to P value significance where the bigger the node size the smaller the P value from the gene‐based meta‐analysis result. Nodes (circles) represent genes that are among the top genes in the consensus network. Edges (lines) are known protein–protein (PPI) interactions. Nodes filled in black represent genes robust to exclusion of UBC from the PPI network.

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