Journal of Obesity & Metabolic Syndrome


J Obes Metab Syndr 2023; 32(1): 98-102

Published online March 30, 2023

Copyright © Korean Society for the Study of Obesity.

Transcriptomic Profiling of Subcutaneous Adipose Tissue in Relation to Bariatric Surgery: A Retrospective, Pooled Re-analysis

Youdinghuan Chen 1,2,3,*

1Faculty of Bioinformatics and Data Science, College of Health Professions and Natural Sciences, Wilmington University, New Castle, DE; 2National Coalition of Independent Scholars, Battleboro, VT; 3Faculty of Sciences, Mathematics and Biotechnology, University of California-Berkeley Extension, Berkeley, CA, USA

Correspondence to:
Youdinghuan Chen
Faculty of Sciences, Mathematics and Biotechnology, University of California-Berkeley Extension, 1995 University Avenue Ste 130, Berkeley, CA 94704, USA
Tel: +1-510-642-4111

Received: November 12, 2022; Reviewed : December 10, 2022; Accepted: January 7, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Bariatric surgery is the most effective intervention for weight loss possibly through modulating subcutaneous adipose tissue (SAT) molecular programs. The post-operative molecular and biological impacts, including gene expression, deserve in-depth investigation especially given the small sample sizes in the literature.
Methods: Five existing datasets (n=237 SATs) were re-processed and corrected for batch-to-batch variation. Unsupervised approaches and robust linear mixed effect model were used to compare gene expression post- (n=126) to pre-operation (n=111).
Results: Post-operative SATs showed distinct global gene expression. Forty-four and 395 genes were over- and under-expressed post-operation (all Bonferroni P<0.05). The under-expressed genes significantly enriched for 21 biological processes/pathways (all Bonferroni P<0.05), 17 (76.2%) and two (9.5%) directly involved in immunity and amino/proteo-glycan metabolism, respectively.
Conclusion: Post-operative SATs might adopt distinct transcriptomic landscapes and undergo a reduction in immune-related processes and amino/proteo-glycan metabolism.

Keywords: Bariatric surgery, Gene expression profiling, Subcutaneous fat, Adipose tissue, white

Obesity is a rising threat to public health with comorbidities including type 2 diabetes and hypertension.1 To date, bariatric surgery is the most effective procedure to treat obesity and achieve weight loss.1,2 The benefits of bariatric surgery, including improved outcomes from multiple comorbidities and reduced all-cause mortality, far outweigh the low short-term risks and rare complications.1

The molecular and cellular mechanisms behind the effectiveness of bariatric surgery remain elusive.3 One hypothesis is that the bariatric procedure reprograms metabolic networks in adipose tissue. Subcutaneous adipose tissue (SAT) is a major subtype of human adipose with an important role in fat storage and has been implicated in obesity.4,5 Proteome-wide profiling, validated by targeted experiments, showed substantially reduced fat stored in SAT after bariatric surgery.2

Transcriptome-wide studies also attempted to address the biological changes after bariatric surgery.3-6 However, existing studies in SAT profiling suffer from small sample sizes with as few as eight subjects measured in multiple batches.4 The present study leverages publicly available transcriptomic datasets and conducts pooled profiling with rigorous statistics to investigate the effect of bariatric surgery on SAT gene expression.

Datasets access and processing

Summarized in Fig. 1A, Supplementary Table 1, five datasets (GSE29411, GSE42715, GSE65540, GSE66921, and GSE199063) were publicly accessed from Gene Expression Omnibus (www.ncbi. The study is Institutional Review Board exempt because all clinical information was de-identified, previously published, and publicly available. Further processing of gene expression data was performed in two phases. First, genes coded outside of chromosomes 1-22 and X, measured by control probes, without the Hugo/RefSeq annotation, or having flat-zero expression were excluded. Expression values of genes with duplicate names were mean-aggregated, standard-normalized, and subjected to a 0.5% least-variant filter. Second, all datasets were inner-joined followed by batch-effect correction (sva-ComBat3.38.08), constrained within ±5.0, and standard-scaled. Non-SATs and one SAT with distinct global expression were excluded, leaving n=237 from 118 unique subjects. Pre-operative (PreOP) SATs were defined as being collected before or at the time of bariatric surgery, while post-operative (PostOP) SATs were collected 3 to 60 months after surgery.

Unsupervised analyses

The first two principal components (PCs) were computed on all 5,000 genes with R function ‘prcomp.’ PC regions were identified based on whether a sample is above or below the negative identity line. Unsupervised hierarchical clustering with Manhattan distance and Ward-D linkage was performed on 1,188 (23.8%) genes with inter-sample standard deviation exceeding 0.99 (‘pheatmap1.0.12’).

Differential gene expression

A robust linear mixed effect model (LIMMA3.46.0)9 was used to identify differentially expressed genes in PostOP SATs with explicit consideration of the 49.8% (118 of 237) samples from repeated measurements, as follows:

PostOPyes=FixedEffect (gene)+RandomEffect (subject, withinsubject correlation coefficient)

The significantly over- and under-expressed genes, identified at Bonferroni P<0.05, were visualized with EnhancedVolcano1.8.0 and separately tested for Gene Ontology biological processes noredundant and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment using all 5,000 genes as the background set on the WebGestalt server (

Statistical analysis

Two-sided Welch’s t and Fisher’s exact tests were used to evaluate differences and associations, respectively. All analysis code was executed in R4.0.3 and available at

SAT global gene expression showed clearcut differences before and after bariatric surgery. The first two PCs could be delineated by a simple, linear decision boundary, and the regions created were significantly associated with PreOP/PostOP status (odds ratio [OR], 7.18; 95% confidence interval [CI], 3.93 to 13.42; Fisher’s exact P=1.91E-12) (Fig. 1B). Both PCs were significantly lower in Post- OP (both Welch t-test P<5E-7) (Fig. 1C). Clustering of the most variable genes segregated SAT samples into groups significantly associated with PreOP/PostOP status (OR, 3.65; 95% CI, 1.94 to 7.06; Fisher’s exact P=1.44E-5) (Fig. 1D). There was no detectable association between expression and dataset/batch (Fig. 1D, Supplementary Fig. 1).

A robust linear mixed effect model adjusting for repeated measurements identified 44 (0.9%) over- and 395 (7.9%) under-expressed genes in PostOP SATs (all Bonferroni-adjusted P<0.05) (Fig. 1E, Supplementary Table 2). Gene Ontology identified 18 significant biological processes associated with the under-expressed genes (all OR >2.1 and Bonferroni P<0.05), 14 (77.8%) playing a direct role in immunity and one (5.6%) in amino/proteo-glycan metabolism (A in Table 1). KEGG identified two (66.7%) immune-related and one (33.3%) amino/proteo-glycan metabolic pathways (all OR >2.6 and Bonferroni P<0.05) (B in Table 1). No significant ontology or pathway was associated with the overexpressed genes.

Obesity is a global health problem with many comorbidities. Bariatric surgery, the most effective obesity intervention to date, remains underexplored. Through a pooled re-analysis maximizing sample size and power, this study confirms the gene and pathwaylevel changes post-surgery despite the heterogeneity in measurement and study designs.

All unsupervised analyses support a distinct global transcriptomic landscape associated with bariatric surgery. Many genes, including those with a known role in obesity, showed differential expression. CIRBP involved in liver glucose metabolism11 was the most overexpressed PostOP. SIK2, under-expressed in individuals with obesity and negatively associated with diabetes,12 was the second. These findings could imply bariatric surgery’s efficacy through related metabolic processes.

Many downregulated genes, including receptor CD4, were immune- related and enriched for immune-related biological processes. Notably, “granulocyte activation” and “neutrophil-mediated immunity” showed the highest enrichment and significance (OR >3.6 and Bonferroni P<4E-14). These findings, alongside previous reports,3,6 provide insights into how the bariatric procedure might yield beneficial outcomes through cellular-molecular modulation. Other downregulated processes were involved in intercellular signaling and could broadly contribute to proper immune function. Both Gene Ontology and KEGG also detected reduced amino/ proteo-glycan metabolism in PostOP SATs, consistent with prior knowledge.13

This study has limitations. First, potential confounders including obesity severity, the type of bariatric surgery, and the presence of other illnesses were not addressed due to limited data availability. Second, the biological findings of this study, especially the downregulated immune-related pathways, need elucidation partly because acquiring SATs for expression analysis requires a biopsy. Since molecular changes such as wound healing can be detected after solidtissue biopsy,14 future studies should consider evaluating less invasive procedures (e.g., blood sampling) that can detect bariatric surgery- associated changes with minimal bias. It will also be interesting to identify the specific cell types that contribute to such molecular changes, possibly through novel cell-type deconvolution methods, single-cell approaches, and integration of other molecular data types.

The author would like to thank the peer reviewers and the editorial team for their input.

Fig. 1. Gene expression landscape of subcutaneous adipose tissues (SATs) after bariatric surgery. (A) Summary of pooled data processing workflow yielding 5,000 genes in 126 post-operative (PostOP) and 111 preoperative (PreOP) SATs. (B) Global expression represented by the first two standard-normalized principal components (PC). Dashed line is the negative identity line linearly separating SATs into regions A (above) and B (below) tested against PreOP/PostOP status by Fisher’s exact test. (C) Welch’s t-test comparison of normalized PCs in PostOP to PreOP. (D) Heat map of 1,188 most variable genes. High (H) and low (L) expression clusters were determined by the unsupervised hierarchical method and associated with PreOP/PostOP status by Fisher’s exact test. (E) Volcano plot showing 44 over- and 395 under-expressed genes identified by the robust linear mixed effect model. OR, odds ratio; NA, not applicable.

Biologically relevant processes associated with the 395 downregulated genes in SATs after bariatric surgery

Term ID Description Direct role Available no. Observed no. Expected no. OR Raw P Bonferroni P
(A) Gene Ontology: biological processes
GO:0036230 Granulocyte activation Immunity 129 41 11.2 3.66 2.40E-14 2.00E-11
GO:0002446 Neutrophil-mediated immunity Immunity 130 41 11.3 3.64 3.24E-14 2.70E-11
GO:0002764 Immune response-regulating signaling pathway Immunity 148 36 12.8 2.80 4.76E-09 3.97E-06
GO:0006909 Phagocytosis Immunity 78 23 6.8 3.40 7.58E-08 6.32E-05
GO:0019882 Antigen processing and presentation Immunity 76 21 6.6 3.19 9.49E-07 7.92E-04
GO:0002694 Regulation of leukocyte activation Immunity 162 33 14.1 2.35 1.87E-06 1.56E-03
GO:0070661 Leukocyte proliferation Immunity 79 21 6.9 3.06 1.90E-06 1.59E-03
GO:0042110 T cell activation Immunity 151 31 13.1 2.37 3.29E-06 2.75E-03
GO:0050867 Positive regulation of cell activation Signaling 109 25 9.5 2.64 3.86E-06 3.22E-03
GO:0006898 Receptor-mediated endocytosis Immunity 89 22 7.7 2.85 4.05E-06 3.38E-03
GO:2000147 Positive regulation of cell motility Signaling 189 35 16.4 2.13 8.91E-06 7.43E-03
GO:0007159 Leukocyte cell-cell adhesion Immunity 115 25 10.0 2.51 1.07E-05 8.92E-03
GO:0051701 Interaction with host Immunity 71 18 6.2 2.92 2.14E-05 0.0179
GO:0050900 Leukocyte migration Immunity 142 28 12.3 2.27 2.23E-05 0.0186
GO:0002576 Platelet degranulation Immunity 54 15 4.7 3.20 3.29E-05 0.0274
GO:0031589 Cell-substrate adhesion Signaling 139 27 12.1 2.24 4.15E-05 0.0346
GO:0006022 Aminoglycan metabolic process Metabolism 56 15 4.9 3.09 5.26E-05 0.0439
GO:0002683 Negative regulation of immune system process Immunity 119 24 10.3 2.32 5.96E-05 0.0497
(B) KEGG pathways
hsa04145 Phagosome Immunity 33 15 3.3 4.58 1.10E-07 3.24E-05
hsa05205 Proteoglycans in cancer Metabolism 77 22 7.6 2.88 2.03E-06 5.97E-04
hsa04062 Chemokine signaling pathway Immunity 65 17 6.5 2.63 1.11E-04 0.033

All processes reached Bonferroni-adjusted P< 0.05.

SAT, subcutaneous adipose tissue; OR, odds ratio; KEGG, Kyoto Encyclopedia of Genes and Genomes.

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