Abstract

Background

   Sarcopenia is characterized by the age-associated loss of skeletal
   muscle mass and strength that develops progressively and plays an
   important role in the disability of the elderly. It has received
   growing attention over the last decade and has been implicated as both
   a cause and consequence of type 2 diabetes mellitus (T2DM). The
   existence of T2DM could increase the risk of developing sarcopenia
   through multiple mechanisms including advanced glycation end-product
   accumulation. Meanwhile, sarcopenia would alter glucose disposal and
   may contribute to the development and progression of T2DM due to
   reduced muscle mass.

Methods

   We implemented transcriptomic analysis of skeletal muscle biopsy
   specimens in sarcopenia patients and proliferating myoblasts or
   differentiated myotubes from individuals with T2DM. Related microarray
   data were selected from Gene Expression Omnibus (GEO) to screen the
   genes, which were differentially expressed for sarcopenia and T2DM.
   Multiple combinatorial statistical methods and bioinformatics tools
   were used to analyze the common DEGs. Meanwhile, functional enrichment
   analysis was also carried out. Furthermore, we constructed the
   protein-protein interaction (PPI), as well as transcription factor
   (TF)-gene interactions network and TF-miRNA coregulatory network.
   Finally, based on the common DEGs, drug compounds were speculated using
   the Drug Signatures database (DSigDB).

Results

   A total of 1765 and 2155 DEGs of sarcopenia and T2DM were screened,
   respectively. 15 common genes (LXN, CIB2, PEA15, KANK2, FGD1, NMRK1,
   PLCB1, SEMA4G, ADARB1, UPF3A, CSTB, COL3A1, CD99, ETV3, FJX1)
   correlated with sarcopenia and T2DM simultaneously were then
   identified, and 3 genes (UPF3A, CSTB and PEA15) of them were regarded
   as hub genes. Functional enrichment analysis revealed several shared
   pathways between two diseases. In addition, according to the TF-gene
   interactions network and TF-miRNA coregulatory network, part of TF and
   miRNA may be identified as key regulator in sarcopenia and T2DM at the
   same time (e.g., CREM and miR-155). Notably, drug compounds for T2DM
   and sarcopenia were also suggested, such as coenzyme Q10.

Conclusion

   This study revealed that sarcopenia and T2DM may share similar
   pathogenesis and provided new biological targets and ideas for early
   diagnosis and effective treatment of sarcopenia and T2DM.

Introduction

   Primarily aging-associated skeletal muscle changes are muscle atrophy,
   usually accompanied by sarcopenia. Sarcopenia is characterized by the
   age-related progressive loss of skeletal muscle mass and strength which
   results in muscle weakness, restricted mobility, and increased
   susceptibility to injury [[28]1]. It is estimated that about 50 million
   people suffering from sarcopenia in the world at present, and the
   number is expected to reach 500 million by the year 2050. Data show
   that one-third of people aged 65 and above had sarcopenia (the
   incidence rate was 14% to 33%), and the prevalence rate of the elderly
   aged 80 years and above was as high as 50%-60% [[29]2]. The concept of
   ‘Sarcopenia’ was first defined as the age-related loss of muscle mass
   by Rosenberg in 1997 [[30]3]. Later, organizations, such as the
   International Working Group on Sarcopenia (IWGS) and the European
   Working Group on Sarcopenia in Older People (EWGSOP), expanded the
   definition of sarcopenia with muscle function, including muscle
   strength and physical performance [[31]4,[32]5]. However, it is hard to
   reach an agreement on the definition of sarcopenia, since the experts
   in these organizations propose different thresholds and diagnostic
   tests for the evaluation of muscle mass, muscle strength, and physical
   performance. Several reasons that contribute to sarcopenia include
   increased inflammation [[33]6], excessive oxidative stress,
   mitochondrial dysfunction [[34]7], reduced muscle capillarization
   [[35]8], and changes in dietary intake of protein [[36]9].

   T2DM, a common metabolic disease, is usually accompanied by insulin
   resistance, activated inflammation, advanced glycation end-product
   (AGE) accumulation and increased oxidative stress. These mechanisms
   would result in activated inflammation, mitochondrial and vascular
   dysfunction, and impairments in protein metabolism, which cause a
   problem to muscle health, e.g., muscle mass, muscle strength, muscle
   quality, and muscle function [[37]10]. Specifically, the anabolic
   action of insulin in skeletal muscle may be progressively lost in T2DM
   due to the impaired insulin sensitivity. Additionally, the impaired
   insulin action may induce increased protein degradation and decreased
   protein synthesis, which leads to reduction in muscle mass and strength
   [[38]10]. Moreover, the results from the hind limb muscles of diabetic
   mice revealed that the decline in muscle mass, muscle endurance and
   regenerative capacity were related to AGEs accumulation [[39]11]. Thus,
   the risk of sarcopenia is increased in T2DM patients [[40]10,[41]12].
   On the other hand, skeletal muscle is essential for glucose clearance
   and is in charge of over 80% of glucose uptake from postprandial
   glucose load, and consequently, it has been proved that sarcopenia
   would alter glucose disposal by lowering muscle mass and also increase
   localized inflammation, which may contribute to the development and
   progression of T2DM [[42]13].

   Since various bidirectional associations between sarcopenia and T2DM
   exist, and the fact that one condition can increase the possibility of
   developing the other [[43]12], our study is aiming to find biological
   pathways and the relationship between sarcopenia and T2DM and provide a
   new idea for the diagnosis and treatment of sarcopenia. Firstly, two
   datasets, [44]GSE1428 [[45]14] and [46]GSE166467 [[47]15], were
   selected for finding DEGs for regulating skeletal muscle in sarcopenia
   and T2DM, respectively. Then, common DEGs were found out, and with
   these common DEGs, further pathway enrichment analysis was performed to
   investigate the biological processes of genome-based expression. The
   network of protein-protein interactions (PPIs) was built from these
   common DEGs to select the hub genes. Then, transcription factors and
   miRNA were also traced based on the common DEGs in the PPIs network.
   Finally, potential drugs were suggested via the DSigDB database. The
   sequential workflow of our research was presented in [48]Fig 1.

Fig 1. Workflow of the whole study.

   [49]Fig 1
   [50]Open in a new tab

   DEGs, differentially expressed genes; GO, Gene Ontology; PPI,
   protein-protein interaction; TF, transcription factor.

Material and methods

Data collection

   [51]GSE1428 and [52]GSE166467 datasets were assembled from the GEO
   database [[53]16]. [54]GPL96 (Affymetrix Human Genome U133A Array)
   platform was applied to [55]GSE1428 dataset microarray analysis
   [[56]14] and the [57]GPL10558 (Illumina HumanHT-12 V4.0 expression bead
   chip) platform was used for [58]GSE166467 [[59]15]. [60]GSE1428
   dataset, which illustrates skeletal muscle sarcopenia in
   transcriptional responses, was contributed by Giresi et al.
   [61]GSE166467 for T2DM was presented by Ling et al. The skeletal muscle
   sarcopenia dataset ([62]GSE1428) provided microarray data from vastus
   lateralis muscle biopsies of young and old people. The T2DM dataset
   ([63]GSE166467) contained data from both proliferating myoblasts and
   differentiated myotubes from individuals with T2DM and controls. In
   addition, before the DEGs analysis, the RMA algorithm was used to
   normalize the datasets from raw data if necessary.

Identification of common DEGs between sarcopenia and T2DM

   We achieved the identification of DEGs for the [64]GSE1428 and
   [65]GSE166467 datasets using GEOquery [[66]17] and limma [[67]18]
   packages in R. P-value<0.05 was considered as cutoff criteria to
   determine significant DEGs. Besides, common DEGs were also acquired and
   displayed in venn diagram with R software.

Enrichment analysis of gene ontology and pathways

   Gene set enrichment analysis is a comprehensive resource for collected
   gene sets that accumulate biological pathways for biological
   discoveries [[68]19]. Gene ontology is a kind of functional enrichment
   clustered into three subsections of biological process, molecular
   function and cellular component [[69]20]. In addition, four major
   databases, including KEGG (Kyoto Encyclopedia of Genes and Genomes),
   WikiPathways [[70]21], Reactome [[71]22] and BioCarta, were used to
   specify the shared pathways between sarcopenia and T2DM. GO terms and
   enriched signaling pathways were completed through the Enrichr web
   server ([72]https://amp.pharm.mssm.edu/Enrichr/) for shared DEGs. The
   top 10 listed pathways with the smallest P-value are exhibited.

PPI network construction

   Finding the functions of protein is the primary step in systems biology
   and drug discovery [[73]23]. Protein-protein interaction (PPI) and the
   obtained network are crucial in the field of biological processes at
   many levels of cellular structure and function, including basic
   metabolism and cell differentiation [[74]24,[75]25]. Similar DEGs in
   sarcopenia and T2DM were identified through the NetworkAnalyst platform
   ([76]https://www.networkanalyst.ca/) and organized into a visual PPIs
   network [[77]26]. Then, the PPIs network was further visualized and
   integrated with the Cytoscape platform ([78]https://cytoscape.org/) to
   analyze the protein interactions and genetic interactions [[79]27].

Establishment of the topological algorithm and identification of hub nodes

   In this work, the highly interconnected hub nodes [[80]28] were
   determined by cytoHubba, a plugin of Cytoscape software. Nodes in the
   network could be ranked by 11 topological algorithms with cytoHubba
   ([81]https://apps.cytoscape.org/apps/cytohubba) [[82]29]. The prominent
   modules are located at the position in the PPI network where the
   interconnect density of the hub nodes is highest.

Recognition of related transcription factors and miRNAs

   TFs and miRNAs constitute two major regulation modes of gene
   expression, including transcription and post transcription. To choose
   the TFs and miRNAs, a co-regulatory network has been visualized by the
   NetworkAnalyst platform, which has been widely used as a bioinformatics
   tool [[83]30,[84]31].

Evaluation of candidate drugs

   Drug compounds recognition is the vital module of this study. According
   to the common DEGs for sarcopenia and T2DM which are determined in the
   PPIs network, drug molecules could be predicted based on the DSigDB
   database [[85]32]. In this work, the access of the DSigDB database was
   obtained on the Enrichr platform
   ([86]https://maayanlab.cloud/Enrichr/), which is widely used to
   represent multiple visualization details on gathered functions for the
   genes [[87]33].

Results

Identification of DEGs and shared genes between sarcopenia and diabetes

   [88]GSE1428 dataset was normalized by the RMA algorithm ([89]S1 Fig)
   before DEGs were explored for sarcopenia. A total of 1765 DEGs were
   obtained with 1055 upregulated and 710 downregulated genes, which
   satisfied the screening criteria. For T2DM, the mRNA expression level
   in either proliferating myoblasts or differentiated myotubes from
   [90]GSE166467 with T2DM (n = 13) versus controls (n = 13) was compared.
   Collected 147 genes for interaction genes of sarcopenia versus
   proliferating myoblasts and 54 genes for interaction genes of
   sarcopenia versus differentiated myotubes were compared using R,
   followed by identification of 15 common DEGs (LXN, CIB2, PEA15, KANK2,
   FGD1, NMRK1, PLCB1, SEMA4G, ADARB1, UPF3A, CSTB, COL3A1, CD99, ETV3 and
   FJX1) ([91]S1 Table). Venn diagram displayed the overlap of DEGs from
   sarcopenia, differentiated myotubes and proliferating myoblasts
   ([92]Fig 2A). The heat map for the shared common genes showed
   paralleled transcriptional signature among most of these genes
   according to the log fold change ([93]Fig 2B).

Fig 2. Identification of shared DEGs between sarcopenia and diabetes.

   [94]Fig 2
   [95]Open in a new tab

   (A) Overlap of DEGs represented through a Venn diagram. 15 genes were
   found common from 147 genes for interaction genes of sarcopenia versus
   proliferating myoblasts and 54 genes for interaction genes of
   sarcopenia versus differentiated myotubes. (B) Heat map for the shared
   DEGs of sarcopenia and T2DM according to the log-fold change.

Functional enrichment analysis

   The analysis of gene functional enrichment was carried out on the
   Enrichr platform. The ongoing study analyzed GO terms and enriched
   pathways for 15 common DEGs (LXN, CIB2, PEA15, KANK2, FGD1, NMRK1,
   PLCB1, SEMA4G, ADARB1, UPF3A, CSTB, COL3A1, CD99, ETV3, FJX1). The data
   justified that the common DEGs are mostly enhanced in the regulation of
   G1/S transition of mitotic cell cycle and Rho protein signal
   transduction for the biological process subsection. In the molecular
   function group, the common DEGs are mainly enriched in endopeptidase
   inhibitor activity and protease binding factors. Cellular component
   study exhibited that common DEGs located significantly in the
   nucleolus. Analysis result from KEGG, WikiPathway, Reactome and
   BioCarta pathway was also obtained. The information attained showed the
   AGE-RAGE signaling pathway in T2DM complications significantly (P<0.01)
   assembled in the KEGG pathway database. Functional analysis results of
   GO ([96]Fig 3A) and pathway ([97]Fig 3B) were displayed according to
   -log10(P value) (detailed in [98]S2–[99]S6 Tables).

Fig 3. Functional enrichment analysis of common genes.

   [100]Fig 3
   [101]Open in a new tab

   (A) Enriched GO terms. (B) Pathway analysis through databases of KEGG,
   WikiPathway, Reactome and BioCarta. The results were displayed
   according to -log10(P value).

PPI network to identify hub genes

   The InnateDB database on NetworkAnalyst was employed to construct PPI
   network of the common DEGs, which was then imported into Cytoscape
   software for visualization and optimization. The PPIs network contained
   126 nodes and 127 edges ([102]Fig 4), and it was constructed for hub
   gene identification and drug molecules detection for sarcopenia and
   diabetes in the following steps.

Fig 4. PPI network for common DEGs shared by two diseases.

   [103]Fig 4
   [104]Open in a new tab

   Nodes in purple color indicated common DEGs.

Identification of hub genes

   Hub genes were screened regarding their degree value from the PPI
   network in cytohubba, which was a plugin of Cytoscape software. The top
   three identified hub genes are UPF3A, CSTB and PEA15, which were all
   affiliated to common genes of sarcopenia and proliferating myoblasts of
   T2DM. The network consisted of 72 nodes and 73 edges. Interactions
   between interrelated proteins ([105]Fig 5) and the topological analysis
   result were presented ([106]S7 Table).

Fig 5. Identification of hub genes from PPI network.

   [107]Fig 5
   [108]Open in a new tab

TF-gene interaction network

   TF-gene interaction network was generated using the NetworkAnalyst web
   tool. For the 15 common DEGs (LXN, CIB2, PEA15, KANK2, FGD1, NMRK1,
   PLCB1, SEMA4G, ADARB1, UPF3A, CSTB, COL3A1, CD99, ETV3, FJX1), the
   TF-genes were identified ([109]Fig 6). The network contained 161 nodes
   and 230 edges. CSTB, SEMA4G and ADARB1 were regulated by 62, 38 and 31
   TF-genes, respectively.

Fig 6. TF-gene interaction network based on common DEGs.

   [110]Fig 6
   [111]Open in a new tab

   Nodes in green color indicated common DEGs, and nodes in yellow color
   indicated TF genes.

TF-miRNA coregulatory network

   The main TF-miRNA coregulatory network based on the 15 common DEGs was
   produced via NetworkAnalyst. The network created comprised 289 nodes
   and 409 edges including 15 common DEGs ([112]S2 Fig). Details were
   displayed in [113]S8 Table. This interaction can be used to analyze the
   regulation factor for sarcopenia combined with T2DM. Moreover, the
   subnetwork, which specifically include only hub genes (UPF3A, CSTB and
   PEA15) and their associated miRNA and TF ([114]Fig 7), was extracted
   from the main TF-miRNA coregulatory network.

Fig 7. TF-miRNA coregulatory network including hub genes.

   [115]Fig 7
   [116]Open in a new tab

   The nodes with yellow diamonds are hub genes, red round nodes represent
   miRNA and green round nodes indicate TF.

Identification of candidate drugs

   Drug molecules were detected for 15 common DEGs from the DSigDB
   database on Enrichr platform. Results from the candidate drugs were
   generated, and these drugs represented possible common drugs for
   sarcopenia and diabetes. The analysis depicted that Coenzyme Q10 CTD
   00001167 and dihydroergocristine HL60 UP were the two drug molecules
   with the highest combined score ([117]Table 1).

Table 1. Suggested top drug compounds for sarcopenia.

   Term P-value Adjusted P-value Combined Score Genes
   Coenzyme Q10 CTD 00001167 0.008965 0.141336 611.4626003 ETV3
   dihydroergocristine HL60 UP 0.001684 0.085573 244.4667349 LXN; PLCB1
   (-)-isoprenaline HL60 UP 3.11E-04 0.031049 223.3772771 LXN; ADARB1;
   PLCB1
   Prestwick-983 HL60 UP 3.48E-04 0.031049 211.9492283 LXN; ADARB1; PLCB1
   colforsin MCF7 DOWN 0.022273 0.199012 186.9959577 KANK2
   etacrynic acid HL60 UP 0.023741 0.205246 171.9789874 LXN
   2,2’,5,5’-TETRACHLOROBIPHENYL CTD 00000481 0.027403 0.21128 142.3784815
   PLCB1
   OZONE CTD 00006460 0.028134 0.21128 137.5099228 COL3A1
   TERT-BUTYL HYDROPEROXIDE CTD 00007349 2.39E-05 0.006409 130.1775809
   KANK2;COL3A1; NMRK1;
   UPF3A; ETV3; FJX1; CD99
   spironolactone CTD 00006774 0.029594 0.21128 128.5967412 COL3A1
   [118]Open in a new tab

Discussion

   Sarcopenia-related muscle dysfunction has a great impact on the life
   quality of elderly people. Changes in skeletal muscle development,
   homeostasis and metabolism during aging are reflected in gene
   expression regulation [[119]34]. Sarcopenia has been discovered as a
   new diabetes complication among the elderly population, which makes it
   a major public health event [[120]10]. The study was implemented to
   analyze microarray data of sarcopenia and T2DM. 15 common DEGs were
   identified (including LXN, CIB2, PEA15, KANK2, FGD1, NMRK1, PLCB1,
   SEMA4G, ADARB1, UPF3A, CSTB, COL3A1, CD99, ETV3 and FJX1) to establish
   PPI network, as well as TF-gene interactions network and TF-miRNA
   coregulatory network. In addition, predicted drug compounds of
   sarcopenia and T2DM were suggested.

   Identified 15 common DEGs were selected for exploring GO terms. GO
   terms were selected according to the P-values. For biological process,
   regulation of G1/S transition of the mitotic cell cycle, regulation of
   Rho protein signal transduction, regulation of vitamin D receptor
   signaling pathway were among the top GO term. Evidence revealed that
   the G1-to-S-phase transition is critical for cell proliferation
   [[121]35]. Rho is also called Rho GTPases, which plays an important
   role in the regulation of cytoskeletal recombination and takes part in
   many physiological activities including cell migration, adhesion,
   cytokinesis, proliferation, differentiation and apoptosis [[122]36].
   Vitamin D receptor (VDR), which might be expressed in muscle fibers
   through vitamin D signaling, acts a crucial role in regulating myoblast
   proliferation, differentiation and moderate sarcopenia
   [[123]37,[124]38]. GO terms in terms of molecular function
   endopeptidase inhibitor activity, protease binding and double-stranded
   RNA adenosine deaminase activity were considered to be at the top of
   the list. Top GO terms based on the cellular component were nucleolus,
   nuclear lumen, collagen-containing extracellular matrix.

   The determination of the KEGG pathway was acquired from the 15 common
   DEGs to find similar pathways for both sarcopenia and T2DM. Top 10 KEGG
   pathways included AGE-RAGE signaling pathway in diabetic complications,
   Amoebiasis, Platelet activation, Relaxin signaling pathway, Diabetic
   cardiomyopathy, Nicotinate and nicotinamide metabolism, African
   trypanosomiasis, Carbohydrate digestion and absorption, Endocrine and
   other factor-regulated calcium reabsorption, Long-term depression. The
   AGE-RAGE axis enhances the generation of ROS and a good deal of
   cytokines and chemokines, which lead to local tissue insulin resistance
   [[125]39]. Therefore, this signaling pathway acts a crucial role in the
   pathogenesis of diabetic complications [[126]40]. AGE, known as an
   aging product, also leads to sarcopenia through increasing reactive
   oxygen species generation [[127]39]. At the same time, data from
   WikiPathways showed that the most interacted gene pathways were
   Cell-type Dependent Selectivity of CCK2R Signaling WP3679, GPR40
   Pathway WP3958 and Tryptophan catabolism leading to NAD+ production
   WP4210. Results from the Reactome pathway produced Fatty Acids bound to
   GPR40 (FFAR1) regulate insulin secretion Homo sapiens R-HAS-434316 and
   Free fatty acids regulate insulin secretion Homo sapiens R-HAS-400451.
   BioCarta database hinted that these common genes were enriched in
   Phospholipase C Signaling Pathway Homo sapiens h plcPathway.

   PPI network analysis was the vital section, which helped us detect
   potential hub genes involved in the shared mechanisms of sarcopenia and
   T2DM. Analysis for PPI also came into being for 15 common DEGs and
   results revealed that owing to possessing a high interaction rate and
   high degree value, UPF3A, CSTB and PEA15 genes were selected as hub
   genes, respectively. PEA15 encodes a death effector domain-containing
   protein, which acts as a negative regulator for apoptosis. This encoded
   protein may contribute to insulin resistance in glucose uptake, since
   it is an endogenous substrate for protein kinase C and overexpressed in
   T2DM [[128]41].

   TFs are proteins that control the transcription of DNA into RNA by
   attaching to a particular DNA sequence, hence it is essential for
   regulatory biomolecules [[129]42]. Furthermore, miRNAs participate in
   the regulation of protein expression mainly through binding to target
   sites on an mRNA transcript and inhibiting its translation [[130]43].
   TF-genes and miRNAs play key roles in the ratio of transcription and
   RNA silencing on the post-transcription, respectively. Thus, they are
   significant regulatory biomolecules and even potential biological
   markers [[131]44]. TF-gene interaction network was acquired from the
   common DEGs. According to the network, CSTB has a high interaction rate
   with other TF-genes, displaying a degree value of 62 in the network.
   Among the regulators, CREM had significant interaction with degree
   value of 4. Mechanistically, oxidative stress activation in T2DM
   enhanced CREM expression [[132]45]. In experimental mouse models, CREM
   expression was associated with preventing inflammation and myofibrillar
   protein degradation that affect sarcopenia [[133]46]. Besides, TF-miRNA
   coregulatory network analysis indicated relationships among the common
   DEGs, TFs and miRNAs through visualized TF-miRNA coregulatory network.
   As a result, 206 miRNAs and 68 TF-genes were revealed in this study.
   Among the most interacted TFs, EGR1 has a high degree value of 4. The
   expression of EGR1 is rapidly changed by mechanical stimulation. The
   induction of Sirt1 by EGR1 is necessary for moving away from the
   superfluous reactive oxygen species, which is produced by the
   mechanical stimulus. This activation is lost in aged animals due to the
   loss of EGR1. Therefore, the decreased expression of EGR1 in the
   elderly may contribute to reduced muscle function in sarcopenia
   [[134]47]. The change of miRNA expression in T2DM patients has been
   established by many researchers, and their results revealed that
   miR-155 is crucial for insulin sensitivity regulation of liver, adipose
   tissue, and skeletal muscle. Moreover, a series of clinical researches
   have also found low miR-155 levels in the serum of patients with T2DM,
   which may lead to insulin resistance [[135]48]. Meanwhile, increasing
   evidence has revealed that miRNAs are differentially expressed in
   sarcopenia of the elderly, plasma miR-155 level in the sarcopenia group
   was significantly reduced compared to the non-sarcopenia group
   [[136]49].

   Based on the DSigDB database, drug compounds were put forward. Among
   all the predicted drugs, our study highlighted the top 10 significant
   drugs. Coenzyme Q10 CTD 00001167, dihydroergocristine HL60 UP,
   (-)-isoprenaline HL60 UP, Prestwick-983 HL60 UP, colforsin MCF7 DOWN,
   etacrynic acid HL60 UP, 2,2’,5,5’-TETRACHLOROBIPHENYL CTD 00000481,
   OZONE CTD 00006460, TERT-BUTYL HYDROPEROXIDE CTD 00007349 and
   spironolactone CTD 00006774 were the peak drug candidates for
   sarcopenia and T2DM. A series of studies have shown that coenzyme Q10
   (CoQ10) could prevent oxidative damage, optimize mitochondrial
   functionality and anti-inflammatory effect [[137]50]. These mechanisms
   are related to the physiology and biochemistry of aging muscle and also
   the potential function in preventing sarcopenia [[138]51]. The previous
   study has proven that combining CoQ10 with metformin can improve
   glycemic control by reducing oxidative stress and improving the
   mitochondria morphology [[139]52]. Furthermore, it was found in the
   mouse skeletal muscle transplants that isoprenaline increased the
   volume of regenerated muscle due to the hypertrophic effect [[140]53].
   Insulin-stimulated glucose transport activity in rat adipocytes was
   inhibited by isoprenaline [[141]54]. Therefore, isoproterenol may
   alleviate sarcopenia by improving muscle metabolism and muscle
   function.

   The current study used a lot of bioinformatics methodologies with
   [142]GSE1428, which compared biopsy samples of skeletal muscle between
   the elderly and young people, and [143]GSE166467 which indicated mRNA
   expression in either proliferating myoblasts or differentiated myotubes
   from individuals with T2DM. We have implemented DEGs analysis between
   two databases and tried to find the mechanisms by which two diseases
   interact. Indeed, different reasons (e.g., impaired insulin
   sensitivity, AGEs, subclinical inflammation and mitochondrial
   dysfunction) may be related to this association. Therefore, this work
   is expected to provide guidance for healthcare professionals to
   diagnose sarcopenia at an early stage for elderly patients with T2DM.
   Furthermore, the drug targets are suggested through the identification
   of hub genes. For example, CoQ10 has been shown to be effective in the
   animal models of both T2DM and sarcopenia treatment [[144]50,[145]51].
   Biguanides, a first-line hypoglycemic agent, may protect the
   development of sarcopenia, which is revealed by observational
   cross-sectional research [[146]55]. It is expected that more mature
   T2DM treatment may inspire the treatment of sarcopenia and these drugs
   should be considered for further verification by clinical trials. In
   the future, it is hoped that specific drugs will be developed to treat
   sarcopenia in addition to protein supplementation and exercise.

Conclusion

   This is the first study to explore the relationship between sarcopenia
   and T2DM using transcriptome analysis. We have recognized the common
   genes between sarcopenia and T2DM to explore the association of these
   two diseases. Analysis revealed that sarcopenia and T2DM have similar
   pathogenesis including increased inflammation, excessive oxidative
   stress, mitochondrial dysfunction. Moreover, the existence of one
   disease may increase the risk of developing the other. Besides, some
   drug targets, which are identified based on the hub genes, are
   logically selected since they are probably candidate for drugs that
   already sanctioned. In conclusion, our research provides new biological
   targets and ideas for early diagnosis and effective treatment of
   sarcopenia combined with T2DM.

Supporting information

   S1 Fig. Normalization of the [147]GSE1428 microarray data.

   (TIF)
   [148]Click here for additional data file.^ (161.3KB, tif)
   S2 Fig. The main TF-miRNA coregulatory network.

   The nodes with yellow diamonds are common DEGs, red round nodes
   represent miRNA and green round nodes indicate TF.

   (TIF)
   [149]Click here for additional data file.^ (14.4MB, tif)
   S1 Table. Differentially expressed genes.

   (XLSX)
   [150]Click here for additional data file.^ (75.1KB, xlsx)
   S2 Table. GO annotation analysis details.

   (XLSX)
   [151]Click here for additional data file.^ (30.7KB, xlsx)
   S3 Table. KEGG enrichment analysis details.

   (XLSX)
   [152]Click here for additional data file.^ (14.1KB, xlsx)
   S4 Table. WikiPathway enrichment analysis details.

   (XLSX)
   [153]Click here for additional data file.^ (12.6KB, xlsx)
   S5 Table. Reactome enrichment analysis details.

   (XLSX)
   [154]Click here for additional data file.^ (17.5KB, xlsx)
   S6 Table. BioCarta enrichment analysis details.

   (XLSX)
   [155]Click here for additional data file.^ (9.8KB, xlsx)
   S7 Table. Topological results for top three hub genes.

   (XLSX)
   [156]Click here for additional data file.^ (9.9KB, xlsx)
   S8 Table. Details of the main TF-miRNA coregulatory network.

   (XLSX)
   [157]Click here for additional data file.^ (12.8KB, xlsx)

Acknowledgments