Introduction

Sarcopenia, characterized by age-related loss of skeletal muscle mass and function, and obesity, defined by excessive adipose tissue accumulation, are two major public health challenges increasingly prevalent in aging populations1,2. Both conditions share overlapping pathophysiological mechanisms, including chronic inflammation, oxidative stress, and mitochondrial dysfunction, which contribute to metabolic derangements and functional decline3. Notably, sarcopenia often coexists with obesity (termed sarcopenic obesity), exacerbating the risk of frailty, mobility impairment, and cardiovascular complications4. Despite their clinical relevance, the molecular interplay between sarcopenia and obesity remains incompletely understood, hindering the development of targeted therapeutic strategies.

Recent advancements in transcriptomic profiling have enabled the identification of disease-specific gene signatures and pathways5. In this study, we integrated gene expression datasets from sarcopenia (GSE111016) and obesity (GSE152991) to uncover shared molecular mechanisms. By leveraging bioinformatics analyses, including differential expression analysis, functional enrichment, protein-protein interaction (PPI) network construction, we aimed to identify key genes and pathways underlying the comorbidity of sarcopenia and obesity.

Sarcopenia and obesity are multifactorial conditions influenced by genetic, environmental, and lifestyle factors6,7. Emerging evidence suggests that mitochondrial dysfunction plays a central role in both disorders8,9. For instance, impaired mitochondrial respiration and reduced ATP production contribute to muscle atrophy in sarcopenia, while adipose tissue mitochondrial dysfunction is linked to insulin resistance and metabolic inflexibility in obesity10,11. However, the specific genes and pathways mediating this crosstalk remain unclear.

Transcriptomic studies have identified numerous differentially expressed genes (DEGs) in sarcopenia and obesity. For example, GSE111016, a sarcopenia dataset, revealed DEGs associated with muscle remodeling and energy metabolism, while GSE152991, an obesity dataset, highlighted genes involved in adipogenesis and inflammation. By intersecting these DEGs, we sought to identify conserved molecular signatures that may drive the co-occurrence of sarcopenia and obesity (Fig. 1).

Fig. 1
figure 1

Study workflow diagram. DEGs, differentially expressed genes; CDEGs, common differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction; RT-PCR, reverse transcription-polymerase chain reaction.

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Methods

Data collection

The datasets GSE152991 and GSE111016 used in this study were both obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). For GSE152991, to minimize confounding by metabolic abnormalities, only adipose tissue samples from 11 metabolically healthy lean (non-obese) individuals and 14 metabolically healthy obese individuals (with normal insulin sensitivity) were included, while excluding 20 metabolically unhealthy obese samples with abnormal insulin sensitivity. GSE111016 included skeletal muscle samples from 20 sarcopenia patients and 20 controls, which were used for the identification of DEGs.

Identification of common DEGs between sarcopenia and obesity

The R language “limma” package was used to analyze the data from GSE152991 and GSE111016 to identify DEGs. The filtering criteria were set as an absolute value of log2 Fold Change (log2 FC) > 0.25 and an –log₁₀(p‑value) > 1.25. A Venn diagram was employed to intersect the DEGs based on GSE152991 with those based on GSE111016, thereby obtaining Common Differentially Expressed Genes (CDEGs).

Enrichment analysis of gene ontology and pathways

GO and KEGG enrichment analyses were performed on 208 CDEGs and four identified key CDEGs using the R package “clusterProfiler”. GO analysis categorized gene functions into three categories: biological processes (BP), cellular components (CC), and molecular functions (MF). KEGG analysis mapped genes to molecular interaction networks encompassing seven major categories, with significant pathways defined by a threshold of p < 0.05. Visualization using the R package “ggplot2” showed that enrichment results for both the full CDEG set and key CDEGs were significantly enriched in energy metabolism-related processes (e.g., oxidative phosphorylation, mitochondrial electron transport chain) and showed overlap with neurodegenerative disease pathways.

Construction of protein-protein interaction network

PPI network analysis of the CDEGs was conducted based on the STRING database (https://cn.string-db.org/), a commonly used tool for evaluating protein interactions. The results were further imported into the software Cytoscape v 3.10.3 (https://cytoscape.org/) to construct the PPI network. In the PPI network, nodes represent the CDEGs from the STRING database, while edges (connections between nodes) represent the interactions among different CDEGs.

Screening and correlation analysis of key CDEGs

Based on the constructed PPI network map, ten algorithms (namely MCC, DMNC, MNC, Degree, EPC, BottleNeck, Closeness, Radiality, Betweenness, and Stress) were used to calculate the top 30 ranked CDEGs, respectively. The intersection of these results was taken as the key CDEGs. Pearson correlation analysis was employed to evaluate the correlations among the key CDEGs. In Pearson correlation analysis, the r value (correlation coefficient) was used to assess the magnitude of the effect. The R package “ggplot2” was utilized to plot the correlation scatter plots.

Tissue sample collection

Patients meeting the diagnostic criteria for sarcopenia and obesity, along with age- and sex-matched healthy control individuals, were recruited clinically. Specifically, sarcopenia patients (n = 5, age 65 ± 5 years, BMI 22 ± 3, diagnosed per EWGSOP2 criteria) and obesity patients (n = 6, age 65 ± 5 years, BMI 32 ± 4, metabolically healthy per IDF criteria) were recruited, with matched controls. Full clinical characteristics are in Supplementary material. Following the acquisition of informed consent from all participants, skeletal muscle tissue and subcutaneous adipose tissue samples were collected via surgical resection. Collected samples were immediately frozen in liquid nitrogen and subsequently transferred to an -80 °C freezer for storage until subsequent experiments.

RNA extraction and RT-PCR protocols

Total RNA was extracted from cryopreserved tissue samples ground in liquid nitrogen using the SteadyPure RNA Extraction Kit (AG, AG21024). RNA concentration and purity were assessed via spectrophotometry, ensuring A260/A280 ratios between 1.8 and 2.0. Reverse transcription of RNA to cDNA was performed using the RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific, K1622), following the manufacturer’s specifications for reaction setup and conditions. Specific primers for SDHB, SDHD, ATP5F1A, and ATP5F1B genes were designed (sequences provided in Table 1). Quantitative PCR (qPCR) was conducted using LightCycler 480 SYBR Green I Master (Roche, 04707516001) on a LightCycler 480 II Real-Time PCR System (Roche). GAPDH served as the internal reference gene. Relative expression levels of each selected gene were calculated using the 2−△ΔCt method, comparing sarcopenia patients vs. healthy controls and obesity patients vs. healthy controls. Statistical analysis was performed using Student’s t-test, with p < 0.05 considered statistically significant.

Table 1 Primer sequences for qPCR analysis.
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Statistical methods

The R software (version 4.4.2) and Cytoscape software (v 3.10.3) were utilized for data processing and graphical representation. Pearson correlation analysis was employed to evaluate the correlations among key CDEGs. In all data processing, a p-value of less than 0.05 was considered statistically significant.

Results

Identification of DEGs and shared genes between sarcopenia and obesity

Using the limma R package, we analyzed the sarcopenia dataset GSE111016 and identified a total of 582 DEGs, including 271 significantly upregulated genes and 311 significantly downregulated genes (Fig. 2a). In the obesity dataset GSE152991, 7,332 DEGs were detected, comprising 4,190 upregulated genes and 3,142 downregulated genes. The wider distribution of fold changes indicated more significant perturbation of gene expression associated with obesity (Fig. 2b).

Further analysis revealed that the DEG expression patterns in both sarcopenia and obesity groups were significantly separated from those in the control group, exhibiting disease-specific modular distribution characteristics (Fig. 2d and e). Additionally, intersection analysis of DEGs from the two datasets showed that 208 overlapping DEGs were shared between the 582 DEGs in sarcopenia and the 7,332 DEGs in obesity (Fig. 2c). This result suggests that the two metabolic diseases may share partial core molecular mechanisms, providing important clues for exploring their underlying shared pathophysiological basis.

Fig. 2
figure 2

Analysis and intergroup comparison of DEGs in sarcopenia and obesity datasets. (a) Volcano plot of DEGs in the sarcopenia dataset (GSE111016). (b) Volcano plot of DEGs in the obesity dataset (GSE152991). (c) Venn diagram of DEG intersection analysis between the two datasets. (d) Hierarchical clustering heatmap of DEG expression profiles in the sarcopenia group (Sarcopenia) and non-sarcopenia group (Normal). (e) Hierarchical clustering heatmap of DEG expression profiles in the obesity group (Obesity) and non-obesity group (Normal).

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GO and KEGG analyses of CDEGs

Further GO and KEGG enrichment analyses were performed on the 208 CDEGs (Fig. 2c), yielding the following results: KEGG pathway analysis showed that the CDEGs were significantly enriched in 10 key pathways (Fig. 3d)12,13,14. Among these, the thermogenesis and oxidative phosphorylation pathways exhibited the highest enrichment significance, involving 28 and 32 CDEGs, respectively. Additionally, the analysis revealed significant enrichment in neurodegenerative disease-related pathways, including Parkinson’s disease and Alzheimer’s disease, which overlapped with oxidative stress-related pathways.

Combined with the GO analysis results (Figs. 3a–c), energy metabolism processes such as oxidative phosphorylation and mitochondrial electron transport chain were significantly enriched in biological processes (p < 0.001). In cellular components, gene expression associated with respiratory chain complexes (e.g., cytochrome complex, NADH dehydrogenase complex) was significantly altered. Molecular function analysis also showed significant enrichment of energy metabolism-related functions. Collectively, these results suggest that energy metabolism disorders and activation of neurodegenerative pathways may serve as common molecular foundations for the two metabolic diseases.

Fig. 3
figure 3

GO and KEGG Enrichment Analysis of the CDEGs in the Two Datasets. (a–c) GO enrichment analysis results, showing the enrichment in biological processes (a), cellular components (b), and molecular functions (c). (d) KEGG pathway enrichment analysis results12,13,14.

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Construction of PPI network and acquisition of key CDEGs

A PPI network of the 208 intersecting CDEGs was constructed based on the STRING database and visualized using Cytoscape, revealing complex synergistic regulation among genes (Fig. 4a). Ten topological algorithms (MCC, DMNC, MNC, Degree, EPC, BottleNeck, Closeness, Radiality, Betweenness, Stress) from the CycloHubba plugin were used to rank gene importance (Fig. 4b). By taking the intersection of the top 30 ranked CDEGs from each algorithm, four Key CDEGs were identified: SDHB, SDHD, ATP5F1A, and ATP5F1B (Fig. 4c). All four genes were significantly downregulated in both sarcopenia and obesity groups.

Fig. 4
figure 4

Construction of the PPI network and identification of Key CDEGs. (a) The PPI network of 208 CDEGs was constructed based on the STRING database and visualized using Cytoscape. (b) Ten topological algorithms from the CycloHubba plugin in Cytoscape were used to rank the importance of genes. (c) Four Key CDEGs were identified.

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Pearson correlation analysis of the four Key CDEGs revealed strong positive correlations in the GSE111016 (sarcopenia) dataset: ATP5F1B showed a robust positive correlation with SDHB (r = 0.97, p = 8.5 × 10⁻²⁴), while SDHD and ATP5F1A exhibited lower but significant correlation (r = 0.86, p = 2.2 × 10⁻¹²), indicating tight co-expression among mitochondrial respiratory chain-related genes (Figs. 5a–c). In the GSE152991 (obesity) dataset, ATP5F1A and ATP5F1B showed the highest correlation (r = 0.94, p = 6.8 × 10⁻¹³), with SDHB also strongly correlated with ATP5F1B (r = 0.91, p = 6.8 × 10⁻¹³) (Figs. 5d–e). Both datasets validated the conserved co-regulatory patterns of core genes across different tissues. The functions of these genes were enriched in mitochondrial oxidative phosphorylation, electron transport chain, and other energy metabolism pathways, suggesting that mitochondrial dysfunction may serve as a common pathological basis for sarcopenia and obesity.

Fig. 5
figure 5

Pearson correlation analysis of key CDEGs in sarcopenia and obesity datasets. (a–c) Correlation matrices and scatter plots of four Key CDEGs in the GSE111016 sarcopenia dataset. (d–f) Correlation matrices and scatter plots of four Key CDEGs in the GSE152991 obesity dataset.

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Functional enrichment analysis of key CDEGs

GO and KEGG enrichment analyses showed that the four Key CDEGs (SDHB, SDHD, ATP5F1A, ATP5F1B) were significantly enriched in energy metabolism processes such as oxidative phosphorylation, proton motive force-driven mitochondrial ATP synthesis, and electron transport chain in biological processes (Fig. 6a). In cellular components, they were primarily enriched in mitochondrial structural components such as respiratory chain complexes and proton-transporting ATP synthase complexes (Fig. 6b). In molecular functions, they were closely associated with proton-transporting ATPase activity, electron transfer activity, and quinone binding (Fig. 6c).

KEGG pathway analysis indicated that these genes were significantly enriched in oxidative phosphorylation, thermogenesis pathways, and neurodegenerative disease pathways such as Parkinson’s disease and Alzheimer’s disease, with overlaps in oxidative stress pathways (Fig. 6d)12,13,14. Collectively, the functional and pathway enrichment results of the core genes all point to mitochondrial oxidative phosphorylation and energy metabolism regulation, suggesting that mitochondrial dysfunction may be the common key pathological basis for sarcopenia and obesity.

Fig. 6
figure 6

GO and KEGG enrichment analysis of Key CDEGs. (a–c) Results of GO enrichment analysis, showing enrichment in biological processes (a), cellular components (b), and molecular functions (c). (d) Results of KEGG pathway enrichment analysis12,13,14.

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Verification of key CDEGs expression by qPCR

To validate the expression patterns of core genes identified through bioinformatics analysis in sarcopenia and obesity, qPCR was used to quantitatively analyze the expression of four Key CDEGs (SDHB, SDHD, ATP5F1A, ATP5F1B) in tissue samples. Results showed that the expression levels of these four genes were significantly lower in skeletal muscle samples from sarcopenia patients and adipose tissue samples from obesity patients compared to healthy controls, with the downregulation trend consistent with transcriptome data analysis (GSE111016 and GSE152991). The magnitude of downregulation was slightly greater in sarcopenia (approximately 40–50%, p < 0.001) than in obesity (approximately 50–60%, p < 0.001) (Figs. 7a–h). These results provide direct experimental evidence for the hypothesis that mitochondrial dysfunction serves as a shared pathological basis for the two diseases.

Fig. 7
figure 7

qPCR validation of Key CDEGs in sarcopenia and obesity. (a–d) Relative expression levels of SDHB, SDHD, ATP5F1A, and ATP5F1B in skeletal muscle samples from sarcopenia patients and healthy controls; (e–h) Relative expression levels of the above genes in adipose tissue samples from obesity patients and healthy controls. *Data are presented as mean ± standard deviation (Mean ± SD). *p < 0.05, **p < 0.01, ***p < 0.001, **** p < 0.0001 (independent samples t-test).

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Discussion​

This study integrated transcriptomic data from sarcopenia (GSE111016) and obesity (GSE152991) to identify 208 CDEGs, of which four core genes (SDHB, SDHD, ATP5F1A, ATP5F1B) were significantly enriched in the mitochondrial oxidative phosphorylation pathway. SDHB and SDHD, as key subunits of mitochondrial respiratory chain complex II, participate in electron transport, while ATP5F1A and ATP5F1B, components of the ATP synthase, directly influence energy production15,16,17,18. The significant downregulation of these genes in skeletal muscle from sarcopenia patients and adipose tissue from obesity patients suggests that mitochondrial energy metabolism defects may serve as a common molecular mechanism for the two diseases.

This finding is consistent with previous studies: in sarcopenia, impaired mitochondrial respiration and reduced ATP production lead to muscle atrophy19, with studies showing that decreased ATP5F1A expression increases muscle fat infiltration and inhibits myogenic differentiation17; in obesity, mitochondrial dysfunction in adipose tissue is associated with insulin resistance and metabolic inflexibility20,21,22. SDHB gene mutations are associated with various diseases, including hereditary paraganglioma, pheochromocytoma, gastrointestinal stromal tumor (GIST), and renal cell carcinoma23,24,25. Furthermore, SDHB mutations can induce mitochondrial dysfunction, accumulation of reactive oxygen species (ROS), and tumorigenesis26. SDHD, like SDHB, is also a component of the SDH complex, and its mutations are also associated with the aforementioned diseases. It is worth noting that SDHB mutations often lead to metastatic tumors outside the adrenal glands, while tumors associated with SDHD mutations are mostly benign and frequently occur in the head and neck region. The mechanism underlying this is related to the accumulation of succinate and the stabilization of HIF proteins15,27,28. ATP5F1B abnormalities are associated with an increased risk of metabolic syndrome in obese patients29. This study further validated the expression trends of core genes through qPCR, providing direct experimental evidence for the hypothesis that mitochondrial dysfunction drives the comorbidity of metabolic diseases.

PPI network analysis showed that the four core genes were consistently identified as key nodes by algorithms such as MCC and Degree, and exhibited strong positive correlations, indicating functional synergy. GO/KEGG enrichment analyses revealed their primary involvement in oxidative phosphorylation, electron transport chain, and mitochondrial ATP synthesis, with cross-talk to neurodegenerative disease pathways (e.g., Parkinson’s disease, Alzheimer’s disease)30,31. This may explain the common comorbidity of neuromuscular decline and metabolic disorders in sarcopenia and obesity, suggesting that mitochondrial dysfunction could affect multisystem pathology through an energy metabolism-neuroinflammation axis. Notably, the core genes all belong to mitochondrial complexes II and V, implying that targeting mitochondrial complex assembly or function may represent a therapeutic strategy for metabolic diseases. For example, small-molecule compounds targeting ATP synthase have shown promise in improving energy metabolism in metabolic disease models32,33,34.

qPCR validation revealed a greater downregulation amplitude (40–50%) in sarcopenia skeletal muscle than in obesity adipose tissue (50–60%), possibly reflecting the higher dependency of skeletal muscle on mitochondrial function and thus greater sensitivity to energy metabolism abnormalities35,36. This difference provides a clue for disease stratification: sarcopenia patients may rely more on mitochondrial function restoration to improve muscle quality, while obesity patients require simultaneous regulation of mitochondrial metabolism and inflammatory responses in adipose tissue. Additionally, the PPI network revealed indirect interactions between core genes and inflammation-related genes (e.g., IL-6, TNF-α), supporting the “metabolism-inflammation axis” theory, where the release of mitochondrial damage-associated molecular patterns (DAMPs) triggers immune activation and exacerbates metabolic disorders37,38,39.

This study has several limitations. The sample size in the qPCR validation was small, which inevitably reduces statistical power and increases the possibility of false-negative results; consequently, the results require verification in larger cohorts to ensure generalizability. To address this issue, we plan to validate these genes in an independent cohort of 100 sarcopenia and 100 obesity patients to collect more skeletal muscle and adipose tissue samples, which will enable us to confirm the robustness of the observed gene expression trends and enhance the translational potential of our discoveries.

Focusing solely on skeletal muscle (GSE111016) and subcutaneous adipose tissue (GSE152991) may bias pathway interpretation, as mitochondrial dysfunction in liver or visceral fat could exhibit distinct molecular signatures. For instance, liver-specific oxidative phosphorylation defects are linked to insulin resistance in obesity, but were not captured here, potentially oversimplifying the shared mechanism40,41,42. This limitation highlights that our identified CDEGs may represent tissue-specific markers, and their role in cross-organ crosstalk requires validation in multi-tissue models. Additionally, the specific mechanisms underlying how core genes regulate mitochondrial function—including epigenetic regulation, post-translational modification, and potential transcriptional control—remain unclear and require functional validation in cell models like C2C12 myocytes and 3T3-L1 adipocytes through gene knockout or overexpression experiments43,44,45,46. Looking ahead, integrating single-cell sequencing could dissect cell-type-specific expression heterogeneity of mitochondrial genes, while animal models such as high-fat diet-induced obese mice or muscle-specific mitochondrial deficiency models may help explore the therapeutic potential of intervention targets47,48,49.

In conclusion, this study reveals a shared mitochondrial dysfunction mechanism between sarcopenia and obesity through multi-omics integration and experimental validation, identifying a key gene network centered on SDHB, SDHD, ATP5F1A, and ATP5F1B. Clinically, the downregulation of SDHB and ATP5F1A in both tissues suggests their utility as minimally invasive biomarkers. For example, blood-based assays of these genes could stratify sarcopenic obesity risk or monitor intervention efficacy, given their high correlation (r > 0.86) across datasets. Beyond biomarker potential, targeting mitochondrial oxidative phosphorylation, particularly through small-molecule modulators such as ATP synthase activators, could be prioritized for dual-disease therapy.

Conclusion

Our study reveals mitochondrial oxidative phosphorylation dysfunction as a shared pathological mechanism in sarcopenia and obesity through integrative transcriptomic analysis. Four key genes (SDHB, SDHD, ATP5F1A, ATP5F1B) in mitochondrial respiratory chain complexes are identified and validated by qPCR, showing significant downregulation in both diseases. These findings may provide new biomarkers or potential therapeutic targets for sarcopenia and obesity, highlighting mitochondrial energy metabolism as a cross-disease intervention axis.