Skip to main content

ORIGINAL RESEARCH article

Front. Endocrinol., 24 May 2023
Sec. Systems Endocrinology
This article is part of the Research Topic Machine Learning-Assisted Diagnosis and Treatment of Endocrine-Related Diseases View all 13 articles

The oxidative aging model integrated various risk factors in type 2 diabetes mellitus at system level

Rectified
Yao ChenYao Chen1Lilin YaoLilin Yao1Shuheng ZhaoShuheng Zhao1Mengchu XuMengchu Xu1Siwei RenSiwei Ren1Lu Xie*Lu Xie2*Lei Liu*Lei Liu3*Yin Wang,*Yin Wang1,4*
  • 1Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
  • 2Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
  • 3Intelligent Medicine Institute, Fudan University, Shanghai, China
  • 4Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, The First Hospital of China Medical University, Shenyang, China

Background: Type 2 diabetes mellitus (T2DM) is a chronic endocrine metabolic disease caused by insulin dysregulation. Studies have shown that aging-related oxidative stress (as “oxidative aging”) play a critical role in the onset and progression of T2DM, by leading to an energy metabolism imbalance. However, the precise mechanisms through which oxidative aging lead to T2DM are yet to be fully comprehended. Thus, it is urgent to integrate the underlying mechanisms between oxidative aging and T2DM, where meaningful prediction models based on relative profiles are needed.

Methods: First, machine learning was used to build the aging model and disease model. Next, an integrated oxidative aging model was employed to identify crucial oxidative aging risk factors. Finally, a series of bioinformatic analyses (including network, enrichment, sensitivity, and pan-cancer analyses) were used to explore potential mechanisms underlying oxidative aging and T2DM.

Results: The study revealed a close relationship between oxidative aging and T2DM. Our results indicate that nutritional metabolism, inflammation response, mitochondrial function, and protein homeostasis are key factors involved in the interplay between oxidative aging and T2DM, even indicating key indices across different cancer types. Therefore, various risk factors in T2DM were integrated, and the theories of oxi-inflamm-aging and cellular senescence were also confirmed.

Conclusion: In sum, our study successfully integrated the underlying mechanisms linking oxidative aging and T2DM through a series of computational methodologies.

1 Introduction

Type 2 diabetes mellitus (T2DM) is a chronic endocrine metabolic disease caused mostly by insulin dysfunction. The increasing prevalence of diabetes has resulted in a great economic burden in many countries (1). According to statistics, there are approximately 536.6 million people with diabetes worldwide, and this number is expected to rise to approximately 783.2 million in 2045, with T2DM accounting for approximately 90% (1, 2). Therefore, it is imperative to study the etiology of T2DM in depth.

Various reports have shown that T2DM is closely related to aging, with aging being one of the most vital risk factors for T2DM (3, 4). Adipose tissue (AT) is redistributed during aging, which affects the sensitivity of insulin (5). Furthermore, the normal function of pancreatic beta cells also declines (3), and aging causes inflammation and low nutritional status, affecting the endocrine system (6). Additionally, a series of risk factors for T2DM are vital to other age-related diseases, such as Alzheimer's disease (AD), cardiovascular disease (CVD), and cancer (710).

During the aging process, oxidative stress accumulates, leading to an energy imbalance that is key to T2DM (11, 12). For example, oxidative intermediates can damage pancreatic beta cells and exacerbate insulin resistance (13). Moreover, accumulated reactive oxygen species also accelerate aging-related DNA damage and induce cellular senescence (14, 15). With increasing age, the free radical dynamic balance in cells is gradually broken, causing an increase in free radical concentration and inducing the oxidation reaction, leading to T2DM (16). In addition, oxidative stress is closely interrelated with inflammation (17) by activating multiple transcription factors in the inflammatory response (18). Furthermore, abnormal oxidative stress dysregulates the balance of energy metabolism during T2DM development (1923). In summary, the potential mechanism by which aging-related oxidative stress (often described as “oxidative aging” (24)) triggers T2DM needs to be further studied at the system level (Figure 1A).

FIGURE 1
www.frontiersin.org

Figure 1 (A) Diagram of the hypothetical mechanism. (B) The workflow of our study. (C) The pipeline of integrated oxidative aging model.

With the development of artificial intelligence, many research results on diabetes have utilized machine learning (ML), which can gain useful information from original profiles. ML can be widely used in the risk prediction, prognosis, and treatment of clinical diseases such as cardiovascular disease and cancer (25, 26). Recently, it was reported that ML can predict the occurrence of T2DM and its complications, as well as identify key markers in T2DM (2729). Additionally, Mendelian randomization (MR) is conducive to integrating biological information (30, 31). Although numerous studies have revealed some risk factors/mechanisms associated with T2DM, the underlying mechanism between oxidative aging and T2DM is still unclear and requires further exploration.

To further explore the potential mechanisms between oxidative aging and T2DM, a series of computational studies was performed in this paper (Figures 1B, C): (1) Machine learning was used to identify aging and disease (T2DM) markers. (2) An integrative model was built to further explore essential relationships between oxidative aging (aging-related oxidative stress) and T2DM (Figure 1C). (3) Network analysis, enrichment analysis and sensitivity analysis were used to investigate the underlying mechanisms between oxidative aging and T2DM markers. (4) Relative biological functions of identified oxidative aging markers were further validated across different cancer types. As a result, the underlying mechanisms of T2DM (i.e., nutritional metabolism, inflammatory response, mitochondrial function and protein homeostasis) were integrated, which can also provide key indices in cancers.

Results

2.1 Modeling prediction models and identifying relative biomarkers

The gene expression profiles were obtained from the GEO database, including 489 samples and 12,958 genes (Tables S1S3). These genes were ranked by the ReliefF algorithm, and then the aging predictor and disease predictor were built using the k-nearest neighbors (kNN; k=3 with the correlation distance) algorithm, optimized by 10-fold cross-validation. The accuracy of the aging predictor in the test set was 0.70455 and 0.7279 in the aging and disease predictors (Figure 1; Table 1), respectively. Furthermore, the ROC area under the curve (AUC) for the aging and disease predictor models were 0.7712 and 0.72788 (Figure 2), respectively. As a result, our predictors were sufficiently accurate in both aging and disease models.

TABLE 1
www.frontiersin.org

Table 1 The accuracy of aging predictor and disease predictor.

FIGURE 2
www.frontiersin.org

Figure 2 Machine learning results. (A, B) Aging predictor from our previous study, selecting the number of aging markers. (C, D) The improved inflamm-aging predictor, selecting the number of disease markers. (A, C) Learning curve for the training dataset. (B, D) The ROC curve for the test dataset.

Both aging and disease markers have meaningful biological functions. For example, OSBPL1A (oxysterol binding protein-like 1A, ReliefF weight=0.058) was the top aging marker. OSBPL1A is one of a set of intracellular lipid receptors and is closely related to lipid metabolism and cholesterol metabolism (32, 33). TIGD4 (tigger transposable element derived 4, ReliefF weight=0.0253), as the top disease marker, was related to glycogen metabolism. In sum, the abnormal metabolism of lipids, cholesterol and glycogen can lead to T2DM (34). These results indicated the crucial role of energy metabolism in T2DM.

2.2 Identifying the oxidative-aging risk factors by the integrated prediction model

The integrated oxidative aging model was built to explore essential relationships among aging, oxidative and T2DM markers (details are shown in Materials and Methods 5.3, with a total of 11829 “aging-oxidative-disease” triples). The top 10 aging, oxidative and disease markers are shown in Table 2, including relative experimental details (3543). For example, ADP-ribosylarginine hydrolase (ADPRH) is the top aging marker, participating in the regulation of various cellular processes, including both immunity and aging (44). ADPRH adversely influences the immune system via CD8+ T cells, hence promoting an imbalance in energy metabolism (45). TPST1 (tyrosyl protein sulfotransferase 1) is the top disease marker, catalyzing the posttranslational sulfation of tyrosine residues within acidic motifs of many polypeptides in all multicellular organisms (46). TPST1 promoted the secretion of some cytokines and then induced the inflammatory response (47). COX5A (cytochrome C oxidase subunit 5A) is the top oxidative marker related to mitochondrial function (48), which induces an imbalance in energy metabolism and insulin resistance (35). In addition, the predictor accuracy calculated by the selected disease markers was 0.7662 (Table 1). In sum, these results indicated that the integrated oxidative aging model could identify essential relationships in T2DM, even with enough prediction ability.

TABLE 2
www.frontiersin.org

Table 2 The top 10 aging markers, disease markers and oxidative markers from the integrated oxidative model.

2.3 Sensitivity analysis further highlighted the imbalance of energy metabolism in T2DM

The Markov chain Monte Carlo (MCMC) method was used to evaluate the sensitive relationship between oxidative aging and T2DM. As a result, a series of triples were identified as key components (2501 out of 11829) in the integrated oxidative aging model.

The top 10 sensitive relationships (by calculating the absolute differential frequency) are shown in Table 3, where the top relationship was “OSBPL7-COX7C-TM6SF1” (difference=-0.03935). Additionally, Table 3 also displayed experimental details of relative oxidative markers (4955). OSBPL7 (oxysterol binding protein like 7) is an oxysterol-binding protein-like (OSBPL) family member involved in lipid binding and transport and induces cholesterol efflux (56, 57). COX7C (cytochrome C oxidase subunit 7C) is an enzyme in the electron transport chain related to cellular respiration and is also a potential biomarker of diabetes mellitus (58, 59). Transmembrane 6 superfamily member 1 (TM6SF1) participates in regulating transmembrane transport in macrophages (60). Overall, these results indicated that oxidative stress played an important role in the development of T2DM.

TABLE 3
www.frontiersin.org

Table 3 The top 10 pairs with the greatest absolute difference frequency.

The top sensitive aging, disease, oxidative markers (evaluated by the occurrence times, also along with relative experimental details (6169)) and are also shown in Table 4. For example, the top aging marker was HPS1 (Hermansky-Pudlak Syndrome 1 gene), inducing the biogenesis of lysosome-associated cellular organelles (70), which regulates the aging process through sphingolipids (71). The top disease marker was PPP1R15A (protein phosphatase 1 regulatory subunit 15A). PPP1R15A plays an important role in insulin resistance via energy metabolism (72, 73). The top oxidative marker was ATOX1 (antioxidant 1 copper chaperone). It has been reported that ATOX1 can regulate the copper level in the cell and maintain the redox balance as a defense antioxidant (74, 75). In short, the sensitivity analysis emphasized the crucial relationship among aging, oxidative stress and T2DM.

TABLE 4
www.frontiersin.org

Table 4 The top 10 aging markers with the most paired with oxidative markers after sensitive analysis.

2.4 Underlying oxidative-aging mechanisms based on enrichment analysis

To further explore the underlying mechanisms between oxidative aging and T2DM, the shortest path between each pair of oxidative aging and disease markers was identified, and then enrichment analysis was performed based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and biological process (BP) terms in Gene Ontology (GO). As a result, relative enrichment results were summarized in Figures 3, S1, as well as Tables 5 (7588), 6 (89102) and S4 (7681, 8385, 103, 104, 111, 113), S5 (8991, 94, 96, 103, 105110).

FIGURE 3
www.frontiersin.org

Figure 3 Enrichment analysis of the shortest path of KEGG and BP (A). The top 10 pathway enrichment with the minimum FDR in BP terms (B). The top 10 pathway enrichment with the minimum FDR in KEGG pathways.

TABLE 5
www.frontiersin.org

Table 5 The top 10 enriched KEGG pathways.

TABLE 6
www.frontiersin.org

Table 6 The top 10 enriched BP terms.

The top 10 KEGG pathways are shown in Tables 5, S3. The most enriched KEGG pathway was “Parkinson’s Disease” (enriched in 1213 shortest paths). It has been reported that Parkinson’s disease (PD) and T2DM have common pathological mechanisms (7678, 111). For example, oxidative stress and mitochondrial dysfunction are involved in both T2DM and PD pathogenesis (77). Strikingly, there are also a series of common biological pathways in T2DM, PD and cancer, such as mitochondrial dysfunction and protein homeostasis (112). Furthermore, the most significant KEGG pathway with the minimum FDR was “Leishmania Infection” (FDR=0.0000629) (Figure 3B), indicating the inflammatory response in the immune system (82, 113, 114). Notably, the inflammatory response is also often closely related to cancer (113). The classical aging pathway, the “mTOR signaling pathway” was also enriched in shortest pathway (Figure S2), indicating the interrelationship between oxidative aging and T2DM.

The top 10 BP terms are shown in Tables 6, S4. For example, the top enriched BP term was “Regulation of aerobic respiration” (enriched in 29 shortest paths), which was related to energy and mitochondrial function (96). In addition, reactive oxygen species (ROS) are byproducts of aerobic respiration that control various cellular functions (97). The BP term with the minimum FDR was “Regulation of DNA binding” (FDR=0.0000282) (Figure 3A), which is vital to T2DM by dysregulating mitochondria and energy metabolism (100). Obviously, the accumulation of DNA damage is also a hallmark of cancer (115). Overall, these results identified various aspects of risk factors for T2DM, such as oxidative stress, aging, energy metabolism and immune systems.

2.5 Network markers revealed key mechanisms between aging and T2DM

Network markers were identified by calculating the betweenness in the shortest path of each ‘‘oxidative-disease’’ pair, where the top markers are shown in Table 7. For example, the top network marker was SCD (stearyl-coenzyme A desaturase), which is mainly expressed in adipose tissue and can catalyze the synthesis of monounsaturated fatty acids (116). In addition, SCD can affect lipid metabolism and mediate steroidogenesis, playing an important role in insulin resistance (117, 118). Furthermore, SCD participates in mediating the inflammatory reaction, which promotes the progression of cancer (119). Moreover, there were also a series of shortest paths through SITR1 (Figure S3, where permutation p-value=0.002 and 0, before and after sensitive analysis), which was as a clssical aging marker. Thus, network markers indicate the crucial role of oxidative stress dysfunction, along with energy metabolism, in T2DM.

TABLE 7
www.frontiersin.org

Table 7 The top 10 genes with the highest number before and after sensitive analysis.

2.6 Pan-cancer analysis further verified the mechanism of oxidative aging in T2DM

Pan-cancer analysis was used to further verify the relative functions of T2DM oxidative aging markers in cancer. For example, oxidative aging markers in the integrated model were used to evaluate the survival index across different cancer types. There were 9 out of 15 cancer types with significant results (including BLCA, COAD, KIRC, KIRP, LUSC, PRAD, READ, THCA and UCEC, shown in Figure 4). These results suggest that oxidative aging markers can also be used as relative risk factors in cancer.

FIGURE 4
www.frontiersin.org

Figure 4 The results of survival analysis across different cancer types. (A) BLCA; (B) BRCA; (C) COAD; (D) ESCA; (E) KICH; (F) KIRC; (G) KIRP; (H) LIHC; (I) LUAD; (J) LUSC; (K) PRAD; (L) READ; (M) STAD; (N) THCA; (O) LIHC.

Additionally, both the commonality and specificity across 15 cancer types were investigated based on enrichment analysis. The top 10 common KEGG pathways are shown in Figures 5, S4, where “Alzheimer’s Disease” was the top KEGG pathway. Alzheimer’s disease (AD) and cancer share common risk factors. For example, aging is one of the greatest risk factors for the development of Alzheimer’s disease, and the risk of cancer also increases with increasing age (120). In addition, some cancer patients may have a higher risk of Alzheimer’s disease (121). Figures 6, S2 showed the top 10 common BP terms in 15 cancers. “Regulation of cellular respiration” was the top BP term, indicating the key role of energy metabolism in cancer (122). Cellular respiration participates in energy metabolism and is also a hallmark of many cancers (123). The specific enrichment results within each cancer are also summarized in Tables 8, 9, S6, S7 (112, 120163), indicating a series of oxidative aging-related risk factors in cancer, such as the inflammatory response, energy metabolism and mitochondrial function. Overall, our results highlighted a series of crucial functions related to oxidative aging, which can also be used to study potential mechanisms in cancer.

FIGURE 5
www.frontiersin.org

Figure 5 The enrichment analysis shared by cancers. (A)KEGG pathways enriched in 15 cancers (B). BP terms enriched in 15 cancers.

FIGURE 6
www.frontiersin.org

Figure 6 Summarized mechanisms of oxidative-aging in T2DM Rectangle genes represent aging markers, oval genes represent disease markers, rhombus genes represent oxidative markers, hexagon genes represent network markers with high numbers. Orange arrows indicate the gene involved in nutritional metabolism, yellow arrows indicate the gene involved in inflammation response, red arrows indicate the gene associated with mitochondrial function, green arrows indicate the gene associated with protein homeostasis.

TABLE 8
www.frontiersin.org

Table 8 KEGG pathways in each cancer with the minimum FDR.

TABLE 9
www.frontiersin.org

Table 9 BP terms in each cancer with the minimum FDR.

3 Discussion

It is well known that aging-related oxidative stress plays a crucial role in T2DM (3). However, the essential relationship among aging, oxidative stress and T2DM still needs to be explored in more depth. In this paper, a series of computational methods were performed to explore these relationships in T2DM as well as the relative mechanisms. First, both the aging model and disease model were optimized, and relative aging markers and disease markers were identified. Next, the integrated oxidative aging model was built to identify essential “aging-oxidative-disease” relationships. Finally, network analysis, enrichment analysis, sensitivity analysis and pan-cancer analysis were used to further explore the potential mechanisms between oxidative aging and T2DM. As a result, various risk factors in T2DM were integrated.

Our results highlighted that energy metabolism was vital to the development of T2DM. For example, the integrated oxidative aging model identified a series of key markers in T2DM that were closely related to energy metabolism. OSBPL1A and T1GD4 participate in nutritional metabolism; the former is mainly involved in lipid metabolism and cholesterol metabolism, and the latter is mainly related to glycogen metabolism (3234). ADPRH and PPP1R15A can lead to energy metabolism imbalance (35, 63). COX5A can affect mitochondrial function, and ATOX1 is the redox catalyst, both of which can affect energy metabolism through mitochondrial dysfunction (39, 65). Furthermore, as the top network marker, SCD is mainly expressed in adipose tissue and can catalyze the synthesis of monounsaturated fatty acids (116). It can affect lipid metabolism and mediate steroidogenesis, which plays an important role in insulin resistance (117, 118). SIRT1 was also identified by calculating the betweenness. In MCMC, the greatest difference in the absolute value pair was “OSBPL7-COX7C-TM6SF1”, where OSBPL7 participates in lipid binding and transport (49, 50) and COX7C is related to cellular respiration as a potential biomarker of diabetes (51, 52). The classical energy metabolism pathway, “mTOR signaling pathway”, was also identified using the enrichment analysis, indicating the key interaction between oxidative aging and T2DM.

Protein homeostasis is also involved in the progression of T2DM. For instance, amyloid precursor protein (APP) is an oxidative marker identified by MCMC that promotes the secretion of amyloid proteins (164). SPI1 (Spi-1 Proto-Oncogene) was involved in the negative regulation of protein, which caused restraint of aerobic glycolysis (165) (Figure 3). In summary, both APP and SPI1 are related to protein homeostasis and even accelerate the development of both T2DM and neurodegenerative diseases (NDs). That is, protein homeostasis is a common mechanism in both T2DM and ND (166, 167).

The inflammatory response also plays an important role in the development of T2DM. For example, the aging marker HPS1 affects the biogenesis of lysosome-associated cellular organelles and even participates in regulating cellular inflammation (61, 62). The disease marker TPST1 induces the secretion of some cytokines, along with the inflammatory response (37, 38). TM6SF1, as one of the key markers identified by MCMC, was involved in transmembrane transport in macrophages, thus highlighting the key role of the immune system in T2DM (53).

Furthermore, there are a series of experiments and relative clinical stastic results also revealed significant relationships between the identified oxidative aging markers and T2DM. For example, it has been reported that in vitro oxidative stress in mammalian skeletal muscle leads to substantial insulin resistance to distal insulin signaling and glucose transport activity (p=9.2e-05) (168). Chronic oxidative stress can also leads to decreased responsiveness to insulin, ultimately leading to diabetes reported by Alina Berdichevsky et al (p=0.01) (169). Besides, NFKBIA affects the wound healing in diabetic foot ulceration (DFU) (p=0.006) (170), MYC and SCD are related to pyroptosis and immune infiltration in T2DM (p=0.001) (171). The experiment of Parker C. Wilson et al using single-nucleus RNA sequencing has been revealed that GCH1 is associated with early-stage diabetic nephropathy (p=4.88e-09) (172) In short, our results also presented key clinical indices with the help of the integrated oxidative model.

T2DM is associated with an increased risk of developing cancers, such as COAD, PRAD, and THCA (30). It is well known that T2DM and cancer have common risk factors, such as oxidative stress, energy metabolism, inflammation and protein homeostasis (22, 23, 173). Our results also proved that inflammation and energy metabolism were common risk factors in cancers, and even survival analysis further verified the key role of oxidative aging markers across different cancer types. Oxidative stress may lead to chronic inflammation, which in turn can induce most chronic diseases, including both cancer and T2DM. In addition, oxidative stress can damage the normal function of mitochondria as well as energy metabolism, which plays an important role in the development of T2DM and cancer. In short, various risk factors related to oxidative aging were also confirmed in cancer.

According to the oxi-inflamm-aging theory, the aging process is regulated by chronic oxidative stress, as well as the inflammatory response (174). It is well known that dysregulated oxidative stress triggers a series of signaling pathways, thus leading to pancreatic beta cell damage (175). In addition, the cellular senescence theory also highlights cellular inflammation and the oxidative stress response during the aging process (176, 177). That is, cellular senescence may also play an important role in the pathogenesis of T2DM (i.e., through the mTOR signaling pathway) (177, 178). Furthermore, these risk factors even interact with each other and then promote T2DM. For example, the imbalance of energy metabolism could interact with a series of pathways, such as lipid accumulation, chronic inflammation and insulin resistance, triggering T2DM progression (179). It has been reported that normal homeostasis in the insulin-driven immunometabolic network is vital to the preservation of insulin sensitivity in healthy aging (180). Here, our work also highlighted the interaction between the immune system and energy metabolism in the development of T2DM (Figure 3; Tables 5, 6), which is also crucial in cancer (Figures 4, 5). With the help of the integrated oxidative aging model, our study revealed that oxidative stress was interrelated with various aging-related risk factors in T2DM (Tables 26), such as the inflammatory response, mitochondrial function and protein homeostasis. These results further confirmed both the oxi-inflamm-aging and cellular senescence theories. Overall, potential aging-related mechanisms in T2DM were integrated in the context of oxidative stress (Figure 6).

4 Conclusion

In this study, machine learning was performed to predict aging and T2DM, and then relative biomarkers were identified. An integrated oxidative aging model was built to explore the essential relationship between oxidative aging and T2DM. The key roles of nutritional metabolism, the inflammatory response, mitochondrial function and protein homeostasis in T2DM were highlighted in our work with the help of sensitivity analysis, enrichment analysis, network analysis and pan-cancer analysis. In conclusion, various risk factors were integrated in the development of T2DM as well as cancer based on oxidative aging.

5 Materials and methods

5.1 Data and preprocessing

All gene expression data were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), including GSE362, GSE15790, GSE18732, GSE29221, GSE29226, GSE29231, GSE37171, GSE38642, GSE76894, and GSE182120. These datasets were from eight different platforms: GPL96, GPL97, GPL8450, GPL9486, GPL6947, GPL570, GPL6244, and GPL17586.

The gene expression profiles were processed as follows:

(1) Only the samples with both the age and phenotype index (i.e., type 2 diabetes versus control) were retained; otherwise, they were deleted.

(2) The gene expression matrix for each dataset was integrated by summarizing the probe number within the gene symbol.

(3) The total data matrix was integrated, and the missing gene expression values were filled with values of 0.

(4) Genes with missing values ≥ 30% were deleted.

(5) The gene expression matrix was transformed by logarithmic transformation if it contained outliers.

(6) Based on the mean and the standard deviation of gene expression for control individuals, the z-score normalization was performed for both T2DM and control samples.

(7) The singular value decomposition (SVD) method was performed to eliminate the intersample variation based on the top three principal components of the control samples.

(8) The z score was then utilized to normalize all samples based on the mean and the standard deviation of the control samples.

(9) The training set and the test set were randomly divided according to a ratio of approximately 2:1.

As a result, a total of 489 samples were obtained, including 208 samples of healthy aged people (age > 50 years old, 145 training datasets + 63 test datasets), 131 samples of healthy young people (age ≤ 50, 90 + 41), 110 samples of T2DM aged people (age > 50, 75 + 35) and 40 samples of T2DM young people (age ≤ 50, 25 + 15), containing 12958 gene symbols (Tables S1S3).

We also obtained paired gene expression (RNAseq) profiles (“Batch effects normalized mRNA data”) and clinical data from the TCGA database through the xena platform (https://xenabrowser.net/hub/). Cancer types with ≥10 adjacent normal samples were retained. As a result, there were 15 cancer types used in this work: BLCA (408 cancer samples and 19 adjacent normal samples), BRCA(1102 + 113), COAD(451 + 41), ESCA(185 + 11), KICH(66 + 25), KIRC(534 + 72), KIRP(291 + 32), LIHC(376 + 50), LUAD(517 + 59), LUSC(504 + 51), PRAD(498 + 52), READ(160 + 10), STAD(414 + 35), THCA(513 + 59) and UCEC(533 + 22). The tumor expression profiles from the same patient were averaged. Genes with missing values ≥30% were deleted.

5.2 Modeling the aging model and disease model

After randomization as well as a random disorder, the healthy population samples were divided into a training dataset and a test dataset. The ratio of training dataset samples to test dataset samples was close to 2:1. The ReliefF algorithm was used to select key features, and then the first 500 models were studied to train predictors. The optimal model was selected by 10-fold cross-validation. To verify the accuracy of the aging predictor, the selected model was verified in the test dataset.

(1) In the aging model, the normal aged group (age>50) was labeled 1, and the young healthy group (age ≤ 50) was labeled 0; in the disease model, the T2DM group was labeled 1, and the control group (age ≤ 50) was labeled 0.

(2) The 12958 genes were sorted by the ReliefF algorithm;

(3) The predictor was generated using the k-nearest neighbor (kNN, k=3, correlation distance) algorithm. The optimal model was selected by 10-fold cross-validation, where the model with the highest accuracy rate was chosen.

(4) The identified features were considered aging and disease markers. As a result, 304 aging markers and 299 disease markers were identified.

5.2 Identifying essential relationships in T2DM by an integrated oxidative aging model

The integrated oxidative aging model was built to identify the essential relationship among aging, oxidative stress and T2DM. The computational pipeline was referred to by Mendelian randomization (MR), although it was not as strict as MR (Figure 1C).

In this model, the aging-related oxidative stress markers were considered oxidative aging markers, where the relative aging/disease markers were identified in “Methods 5.2”. As a result, the essential relationships among aging, oxidative stress and disease (T2DM) markers were identified as key “aging-oxidative-disease” triples in T2DM.

MR is a statistical method for assessing the causal relationship between risk factors and outcomes based on observational data (181, 182). The causal relationships between the instrumental variables, risk factors, and outcome variables were assessed as follows.

(1) There was a correlation between the instrumental variable and the risk factor.

(2) There was no correlation between the instrumental variable and the confounding factor.

(3) There was no correlation between the instrumental variable and the outcome variable after deleting the effect from the risk factor.

Here, the aging marker was used as the auxiliary variable (similar to the instrumental variable in MR), and the oxidative stress markers were used as the candidate risk factor. Then, aging-related oxidative (“oxidative aging”) markers were identified as the risk factor, and disease markers were used as the outcome variable. That is, the integrated oxidative aging model aimed to explore essential relationships among aging, oxidative stress and disease markers in T2DM. This model was performed as follows:

(1) Oxidative markers were obtained as candidate risk factors based on Biological Processes (BP) of Gene Ontology (GO) through the Gene Set Enrichment Analysis (GSEA) platform (http://www.gsea-msigdb.org/gsea/downloads.jsp, “OXIDATIVE” was taken as the keyword). As a result, 310 candidate oxidative markers were selected.

(2) The correlation (differential coexpression) pattern was used to select aging markers that strongly correlated with candidate oxidative stress markers with the help of the Kruskal−Wallis test. Here, the differential coexpression was calculated as follows:

p=KruskalWallis test (aging_marker.oxidative_marker, phenotype)(1)

where the phenotype could be defined as 1 (T2DM) and 0 (control).

Furthermore, both a p-value<0.05 and Benjamini−Hochberg false discovery rate (FDR)<0.1 were used to select strongly correlated aging markers.

(3) To reduce the correlation between the auxiliary variable (aging marker) and confounding factors, as well as further select a strong correlation between the aging marker and the candidate oxidative marker, a permutation test was performed by generating the simulated aging markers from the same number of randomly selected markers to each candidate oxidative marker; this process was repeated 1000 times, and then the p-value was calculated as the proportion of occurrence times (larger than the real mean difference) of the absolute difference between T2DM and control in 1000 permutations. The relationship between each aging marker and the candidate oxidative marker was retained if the permutation P<0.05.

(4) Correlation (differential coexpression) was used to select oxidative markers that strongly correlated with disease markers with the help of the Kruskal−Wallis test. Here, the differential coexpression was calculated as follows:

p=KruskalWallis test (oxidative_marker.disease_marker, phenotype)(2)

where the phenotype could be defined as 1 (T2DM) and 0 (control).

Furthermore, both a p-value<0.05 and Benjamini−Hochberg false discovery rate (FDR)<0.1 were used to select strongly correlated oxidative markers.

(5) To reduce the correlation between the risk factor (oxidative marker) and confounding factors, as well as further select a strong correlation between the oxidative marker and the disease marker, a permutation test was performed by generating the simulated oxidative markers from the same number of randomly selected markers to each disease marker; this process was repeated 1000 times, and then the p-value was calculated as the proportion of occurrence times (larger than the real mean difference) of the absolute difference between T2DM and control in 1000 permutations. The relationship between each aging marker and the candidate oxidative marker was retained if the permutation P<0.05.

(6) The direct relationships for any other factors (genes) were found to reduce the correlation between the auxiliary variable (aging marker) and confounding factors. If there was another factor (gene) that was directly correlated (differentially coexpressed) to both the aging marker and the disease marker, then the relationship from aging to disease was deleted.

p=KruskalWallis test (aging_marker.other_gene, phenotype)(3)
p=KruskalWallis test (disease_marker.other_gene, phenotype)(4)

where the phenotype could be defined as 1 (T2DM) and 0 (control).

Furthermore, both a p-value<0.05 and Benjamini−Hochberg false discovery rate (FDR)<0.1 were used to filter out any direct relationships.

(7) To filter out the effect of horizontal pleiotropy, the aging–disease relationship was further examined by comparing the correlation between each aging and disease marker, through the oxidative marker or otherwise. Herein, steps ①–③ were used to calculate the correlations between auxiliary variables and outcome variables without the background of the risk factor, and step ④ was used to calculate the correlations between auxiliary variables and outcome variables with the context of the risk factor.

① The residual of each disease marker (“residual A”) was calculated based on the oxidative marker:

residual_A=disease_markerb1*oxidative_marker(5)

where b1 is the regression coefficient.

② The residual of each aging marker (“residual B”) was calculated based on the oxidative marker:

residual_B=aging_markerb2*oxidative_marker(6)

where b2 is the regression coefficient.

③ The abovementioned two residuals were further compared, and the residual of the disease marker was calculated (as “residual C”):

residual_C=residual_Ab3*residual_B(7)

where b3 is the regression coefficient.

④ The residual of the disease marker (“residual D”) was calculated based on the aging marker.

⑤ The difference (between “residual C” and “residual D”) was tested between the T2DM and control subgroups using the Kruskal–Wallis test (P<0.05 and FDR<0.1).

Finally, the essential relationship among the aging marker, oxidative marker and disease marker was retained. Thus, 11829 “aging-oxidative-disease” triples were identified, including 105 aging markers, 83 oxidative markers and 282 disease markers. Thus, these 83 oxidative markers were used as oxidative aging markers (risk factors), and 282 disease markers were also used to discriminate the T2DM phenotype.

5.4 Sensitivity analysis using the MCMC method

To further explore the relationship among aging, oxidative stress and T2DM, sensitivity analysis was performed based on the Markov chain Monte Carlo (MCMC) method, where “aging-oxidative-disease” triples identified by MR were further evaluated as a candidate relationship. The MCMC method is used to sample certain posterior distributions in a high-dimensional space based on a given probabilistic background. The key step of MCMC is to construct a Markov chain whose equilibrium distribution is equal to the target probability distribution. The steps were as follows:

(1) Constructing the transfer cores of the ergodic Markov chain. The prior distribution of each parameter was normally distributed based on all identified markers in each group (i.e., T2DM and control), respectively.

(2) Simulate the chains until equilibrium is reached. The Metropolis−Hastings sampling method was used to determine whether the new sample (θ *) was acceptable based on the α value.

α=P(θ*|X)*q(θnθ*)P(θn|X)*q(θnθ*)(8)

where P (θ n | X) and P (θ * | X) are the posterior probability of the nth accepted sample, the new sample q (θ n → θ *) is the transition probability from the nth accepted sample to the new sample, and q (θ * → θ n) is the transition probability from the new sample to the n-th accepted sample.

In this work, the disease score was used to evaluate the simulated samples, with 1000 random samples used as candidate samples for each group (i.e., T2DM or control). The disease score was calculated by comparing the distance between normal and T2DM training samples based on the 282 disease markers identified by the integrated oxidative aging model:

disease_score=k=17distance_of_neareast_neighbour_in_controlk=17distance_of_neareast_neighbour_in_T2DM(9)

(3) Performing the global sensitivity analysis

The correlation index was used to evaluate each “aging-oxidative-disease” triple in the accepted samples (including both T2DM and control):

correlation_index=disease_markeraging_markeroxidative_markeraging_marker(10)

As a result, the correlation index was calculated in each “aging-oxidative-disease” triple for all accepted samples. Then, the Kruskal–Wallis test was used to evaluate each correlation index in each “aging-oxidative-disease” triple, where p-value<0.05 and FDR<0.1 were set as the threshold. Finally, 2501 “aging-oxidative-disease” triples were identified as sensitive relationships, including 41 aging markers, 37 oxidative markers and 61 disease markers.

5.5 Constructing the differential coexpression network

To further reveal the relationship between “oxidative aging” and T2DM, a differential coexpression network was constructed by the following steps:

(1) The Pearson correlation coefficient for each pair of genes was calculated based on the T2DM and control groups.

(2) The Benjamini−Hochberg FDR method was used to adjust the p-values of the correlation coefficient.

(3) The relationship between each gene pair was retained if the coefficient value in T2DM had the opposite sign (i.e., + or -) to that in control, as well as p< 0.05 and FDR< 0.1.

(4) The shortest path between each pair of oxidative aging and disease markers was selected based on the differential coexpression network using the Dijkstra algorithm.

5.6 Enrichment analysis

The gene functions were further explored by enrichment analysis of the shortest pathway. Gene Ontology (GO) terms and KEGG pathways for the GSEA platform were obtained from gene set enrichment analysis (http://software.broadinstitute.org/gsea/downloads.jsp, version 7.5). The hypergeometric distribution was used to test the degree of enrichment of the GO BP and KEGG pathways. Hypergeometric test formula:

P(Xx)=1k=0x1CMk×CNMnkCMk(11)

where N is the total number of genes in the gene set, M is the number of known genes (such as KEGG pathway or BP terms), which is the number of genes identified in each shortest pathway, and k is the number of common genes between known genes and candidate genes identified in each “oxidative-disease” shortest pathway. The p-value of each path was controlled using the Benjamin-Hochberg method. Finally, pathways with p<0.05 and FDR<0.1 were retained.

5.7 Identifying network markers

The subnetwork with the shortest pathways among the selected “oxidative-disease” pairs was constructed, and genes in the subnetwork were sorted by their betweennesses in descending order. To test whether the top betweenness genes were hubs in the background network, we ran a permutation to count the occurrence time of the top genes in the shortest paths between randomly selected genes (containing the same numbers of “oxidative-disease” pairs, based on the identified “aging-oxidative-disease” triples) when they had greater betweennesses than those in our study. We repeated this process 1000 times, and the p-value was calculated as the proportion of occurrence times of the top betweenness genes in 1000 permutations.

5.8 Pan-cancer analysis

The survival analysis was performed based on the oxidative aging markers (identified by the integrated oxidative aging model in 5.3) for each cancer using the Kaplan−Meier method. The tumor samples of each cancer were divided into two groups based on the mean value of the oxidative aging markers. Then, the Kaplan−Meier method was used to evaluate the survival difference between these two groups, and the significance was estimated by the log-rank test. A p-value<0.05 was considered statistically significant.

Genes were considered differentially expressed if they satisfied the following criteria:

(1) Fold change>2;

(2) p-value<0.05 in the Kruskal−Wallis test;

(3) Benjamin-Hochberg false discovery rate (FDR)<0.1.

Then, the differential expression networks were constructed for each cancer, where the details were also the same as 5.5. As a result, each shoreat pathway was selected from each pair of oxidative aging markers and differentially expressed genes (as disease markers in cancer) using the Dijkstra algorithm. Furthermore, enrichment analysis was performed by the “oxidative-disease” shortest pathway for each cancer type, where both p<0.05 and FDR<0.1 were used.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Author contributions

LX, LL, and YW designed the study. YC, LY, SZ and YW analyzed the data. YC, LY and YW interpreted the results. YC, MX, SR and YW visualized the results. All authors wrote and revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by National Natural Science Foundation of China (32000478 to YW) and the Shanghai Municipal Health Commission and Collaborative Innovation Cluster Project (No. 2019CXJQ02), the National Key R&D Program of China (No. 2018YFA0107800), the National Natural Science Foundation of China (No. 81974010), the Provincial Natural Science Foundation of Hunan Province (No. 2021JJ40963). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1196293/full#supplementary-material

Supplementary Table 1 | The detailed datasets used in this work.

Supplementary Table 2 | The gene symbols used in this work.

References

1. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract (2022) 183:109119. doi: 10.1016/j.diabres.2021.109119

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Artasensi A, Pedretti A, Vistoli G, Fumagalli L. Type 2 diabetes mellitus: a review of multi-target drugs. Molecules (2020) 25(8):1987. doi: 10.3390/molecules25081987

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Gunasekaran U, Gannon M. Type 2 diabetes and the aging pancreatic beta cell. Aging (Albany NY). (2011) 3(6):565–75. doi: 10.18632/aging.100350

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Pengpid S, Peltzer K. Prevalence and correlates of undiagnosed, diagnosed, and total type 2 diabetes among adults in Morocco, 2017. Sci Rep (2022) 12(1):16092. doi: 10.1038/s41598-022-20368-4

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Jura M, Kozak LP. Obesity and related consequences to ageing. Age (Dordr). (2016) 38(1):23. doi: 10.1007/s11357-016-9884-3

PubMed Abstract | CrossRef Full Text | Google Scholar

6. van den Beld AW, Kaufman JM, Zillikens MC, Lamberts SWJ, Egan JM, van der Lely AJ. The physiology of endocrine systems with ageing. Lancet Diabetes Endocrinol (2018) 6(8):647–58. doi: 10.1016/S2213-8587(18)30026-3

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Diniz Pereira J, Gomes Fraga V, Morais Santos AL, Carvalho MDG, Caramelli P, Braga Gomes K. Alzheimer's disease and type 2 diabetes mellitus: a systematic review of proteomic studies. J Neurochem (2021) 156(6):753–76. doi: 10.1111/jnc.15166

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Dal Canto E, Ceriello A, Rydén L, Ferrini M, Hansen TB, Schnell O, et al. Diabetes as a cardiovascular risk factor: an overview of global trends of macro and micro vascular complications. Eur J Prev Cardiol (2019) 26(2_suppl):25–32. doi: 10.1177/2047487319878371

PubMed Abstract | CrossRef Full Text | Google Scholar

9. van Eersel ME, Joosten H, Gansevoort RT, Dullaart RP, Slaets JP, Izaks GJ. The interaction of age and type 2 diabetes on executive function and memory in persons aged 35 years or older. PloS One (2013) 8(12):e82991. doi: 10.1371/journal.pone.0082991

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Gallagher EJ, LeRoith D. Obesity and diabetes: the increased risk of cancer and cancer-related mortality. Physiol Rev (2015) 95(3):727–48. doi: 10.1152/physrev.00030.2014

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Frijhoff J, Winyard PG, Zarkovic N, Davies SS, Stocker R, Cheng D, et al. Clinical relevance of biomarkers of oxidative stress. Antioxid Redox Signal (2015) 23(14):1144–70. doi: 10.1089/ars.2015.6317

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Sies H. Oxidative stress: a concept in redox biology and medicine. Redox Biol (2015) 4:180–3. doi: 10.1016/j.redox.2015.01.002

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Giacco F, Brownlee M. Oxidative stress and diabetic complications. Circ Res (2010) 107(9):1058–70. doi: 10.1161/CIRCRESAHA.110.223545

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Beckman KB, Ames BN. The free radical theory of aging matures. Physiol Rev (1998) 78(2):547–81. doi: 10.1152/physrev.1998.78.2.547

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Golden TR, Hinerfeld DA, Melov S. Oxidative stress and aging: beyond correlation. Aging Cell (2002) 1(2):117–23. doi: 10.1046/j.1474-9728.2002.00015.x

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Oliveira BF, Nogueira-Machado JA, Chaves MM. The role of oxidative stress in the aging process. Sci World J (2010) 10:1121–8. doi: 10.1100/tsw.2010.94

CrossRef Full Text | Google Scholar

17. Mancini A, Di Segni C, Raimondo S, Olivieri G, Silvestrini A, Meucci E, et al. Thyroid hormones, oxidative stress, and inflammation. Mediators Inflamm (2016) 2016:6757154. doi: 10.1155/2016/6757154

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Hussain T, Tan B, Yin Y, Blachier F, Tossou MC, Rahu N. Oxidative stress and inflammation: what polyphenols can do for us? Oxid Med Cell Longev (2016) 2016:7432797. doi: 10.1155/2016/7432797

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Grevendonk L, Connell NJ, McCrum C, Fealy CE, Bilet L, Bruls YMH, et al. Impact of aging and exercise on skeletal muscle mitochondrial capacity, energy metabolism, and physical function. Nat Commun (2021) 12(1):4773. doi: 10.1038/s41467-021-24956-2

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Lennicke C, Cochemé HM. Redox metabolism: ROS as specific molecular regulators of cell signaling and function. Mol Cell (2021) 81(18):3691–707. doi: 10.1016/j.molcel.2021.08.018

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Tramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A, et al. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia (2020) 63(3):453–61. doi: 10.1007/s00125-019-05040-3

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Reuter S, Gupta SC, Chaturvedi MM, Aggarwal BB. Oxidative stress, inflammation, and cancer: how are they linked? Free Radic Biol Med (2010) 49(11):1603–16. doi: 10.1016/j.freeradbiomed.2010.09.006

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Tan YT, Lin JF, Li T, Li JJ, Xu RH, Ju HQ. LncRNA-mediated posttranslational modifications and reprogramming of energy metabolism in cancer. Cancer Commun (Lond). (2021) 41(2):109–20. doi: 10.1002/cac2.12108

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Zhou X, Du HH, Jiang M, Zhou C, Deng Y, Long X, et al. Antioxidant effect of lactobacillus fermentum CQPC04-fermented soy milk on d-Galactose-Induced oxidative aging mice. Front Nutr (2021) 8:727467. doi: 10.3389/fnut.2021.727467

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Alaa AM, Bolton T, Di Angelantonio E, Rudd JHF, van der Schaar M. Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK biobank participants. PloS One (2019) 14(5):e0213653. doi: 10.1371/journal.pone.0213653

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med (2021) 13(1):152. doi: 10.1186/s13073-021-00968-x

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Deberneh HM, Kim I. Prediction of type 2 diabetes based on machine learning algorithm. Int J Environ Res Public Health (2021) 18(6):3317. doi: 10.3390/ijerph18063317

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Dagliati A, Marini S, Sacchi L, Cogni G, Teliti M, Tibollo V, et al. Machine learning methods to predict diabetes complications. J Diabetes Sci Technol (2018) 12(2):295–302. doi: 10.1177/1932296817706375

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Li Z, Pan X, Cai YD. Identification of type 2 diabetes biomarkers from mixed single-cell sequencing data with feature selection methods. Front Bioeng Biotechnol (2022) 10:890901. doi: 10.3389/fbioe.2022.890901

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Pearson-Stuttard J, Papadimitriou N, Markozannes G, Cividini S, Kakourou A, Gill D, et al. Type 2 diabetes and cancer: an umbrella review of observational and mendelian randomization studies. Cancer Epidemiol Biomarkers Prev (2021) 30(6):1218–28. doi: 10.1158/1055-9965.EPI-20-1245

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Swerdlow DI. Mendelian randomization and type 2 diabetes. Cardiovasc Drugs Ther (2016) 30(1):51–7. doi: 10.1007/s10557-016-6638-5

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Wang Z, Wang F. Identification of ten-gene related to lipid metabolism for predicting overall survival of breast invasive carcinoma. Contrast Media Mol Imaging (2022) 2022:8348780. doi: 10.1155/2022/8348780

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Tao JH, Wang XT, Yuan W, Chen JN, Wang ZJ, Ma YB, et al. Reduced serum high-density lipoprotein cholesterol levels and aberrantly expressed cholesterol metabolism genes in colorectal cancer. World J Clin Cases (2022) 10(14):4446–59. doi: 10.12998/wjcc.v10.i14.4446

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Fiuza-Luces C, Santos-Lozano A, Llavero F, Campo R, Nogales-Gadea G, Díez-Bermejo J, et al. Muscle molecular adaptations to endurance exercise training are conditioned by glycogen availability: a proteomics-based analysis in the McArdle mouse model. J Physiol (2018) 596(6):1035–61. doi: 10.1113/JP275292

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Gong YY, Liu YY, Li J, Su L, Yu S, Zhu XN, et al. Hypermethylation of Cox5a promoter is associated with mitochondrial dysfunction in skeletal muscle of high fat diet-induced insulin resistant rats. PloS One (2014) 9(12):e113784. doi: 10.1371/journal.pone.0113784

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Peng L, Ma W, Xie Q, Chen B. Identification and validation of hub genes for diabetic retinopathy. PeerJ (2021) 9:e12126. doi: 10.7717/peerj.12126

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Ting TW, Brett MS, Tan ES, Shen Y, Lee SP, Lim EC, et al. Cockayne syndrome due to a maternally-inherited whole gene deletion of ERCC8 and a paternally-inherited ERCC8 exon 4 deletion. Gene (2015) 572(2):274–8. doi: 10.1016/j.gene.2015.07.065

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Akasheh RT, Pini M, Pang J, Fantuzzi G. Increased adiposity in annexin A1-deficient mice. PloS One (2013) 8(12):e82608. doi: 10.1371/journal.pone.0082608

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Nechushtan A, Smith CL, Lamensdorf I, Yoon SH, Youle RJ. Bax and bak coalesce into novel mitochondria-associated clusters during apoptosis. J Cell Biol (2001) 153(6):1265–76. doi: 10.1083/jcb.153.6.1265

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Nagao M, Esguerra JLS, Asai A, Ofori JK, Edlund A, Wendt A, et al. Potential protection against type 2 diabetes in obesity through lower CD36 expression and improved exocytosis in β-cells. Diabetes (2020) 69(6):1193–205. doi: 10.2337/db19-0944

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Zhao Z, Zhang X, Zhao C, Choi J, Shi J, Song K, et al. Protection of pancreatic beta-cells by group VIA phospholipase A(2)-mediated repair of mitochondrial membrane peroxidation. Endocrinology (2010) 151(7):3038–48. doi: 10.1210/en.2010-0016

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Heemskerk MM, Giera M, Bouazzaoui FE, Lips MA, Pijl H, van Dijk KW, et al. Increased PUFA content and 5-lipoxygenase pathway expression are associated with subcutaneous adipose tissue inflammation in obese women with type 2 diabetes. Nutrients (2015) 7(9):7676–90. doi: 10.3390/nu7095362

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Ivashchenko O, Van Veldhoven PP, Brees C, Ho YS, Terlecky SR, Fransen M. Intraperoxisomal redox balance in mammalian cells: oxidative stress and interorganellar cross-talk. Mol Biol Cell (2011) 22(9):1440–51. doi: 10.1091/mbc.E10-11-0919

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Rack JGM, Ariza A, Drown BS, Henfrey C, Bartlett E, Shirai T, et al. (ADP-ribosyl)hydrolases: structural basis for differential substrate recognition and inhibition. Cell Chem Biol (2018) 25(12):1533–1546.e12. doi: 10.1016/j.chembiol.2018.11.001

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Zhang C, Wang L, Liu H, Deng G, Xu P, Tan Y, et al. ADPRH is a prognosis-related biomarker and correlates with immune infiltrates in low grade glioma. J Cancer (2021) 12(10):2912–20. doi: 10.7150/jca.51643

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Ouyang Yb, Lane WS, Moore KL. Tyrosylprotein sulfotransferase: purification and molecular cloning of an enzyme that catalyzes tyrosine O-sulfation, a common posttranslational modification of eukaryotic proteins. Proc Natl Acad Sci USA (1998) 95(6):2896–901. doi: 10.1073/pnas.95.6.2896

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Westmuckett AD, Moore KL. Lack of tyrosylprotein sulfotransferase activity in hematopoietic cells drastically attenuates atherosclerosis in ldlr-/- mice. Arterioscler Thromb Vasc Biol (2009) 29(11):1730–6. doi: 10.1161/ATVBAHA.109.192963

PubMed Abstract | CrossRef Full Text | Google Scholar

48. Xiyang YB, Liu R, Wang XY, Li S, Zhao Y, Lu BT, et al. COX5A plays a vital role in memory impairment associated with brain aging via the BDNF/ERK1/2 signaling pathway. Front Aging Neurosci (2020) 12:215. doi: 10.3389/fnagi.2020.00215

PubMed Abstract | CrossRef Full Text | Google Scholar

49. Aharon-Hananel G, Romero-Afrima L, Saada A, Mantzur C, Raz I, Weksler-Zangen S. Cytochrome c oxidase activity as a metabolic regulator in pancreatic beta-cells. Cells (2022) 11(6):929. doi: 10.3390/cells11060929

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Karslioglu E, Kleinberger JW, Salim FG, Cox AE, Takane KK, Scott DK, et al. cMyc is a principal upstream driver of beta-cell proliferation in rat insulinoma cell lines and is an effective mediator of human beta-cell replication. Mol Endocrinol (2011) 25(10):1760–72. doi: 10.1210/me.2011-1074

PubMed Abstract | CrossRef Full Text | Google Scholar

51. Huard K, Londregan AT, Tesz G, Bahnck KB, Magee TV, Hepworth D, et al. Discovery of selective small molecule inhibitors of monoacylglycerol acyltransferase 3. J Med Chem (2015) 58(18):7164–72. doi: 10.1021/acs.jmedchem.5b01008

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Rouault TA, Tong WH. Iron-sulfur cluster biogenesis and human disease. Trends Genet (2008) 24(8):398–407. doi: 10.1016/j.tig.2008.05.008

PubMed Abstract | CrossRef Full Text | Google Scholar

53. Heitzer T, Krohn K, Albers S, Meinertz T. Tetrahydrobiopterin improves endothelium-dependent vasodilation by increasing nitric oxide activity in patients with type II diabetes mellitus. Diabetologia (2000) 43(11):1435–8. doi: 10.1007/s001250051551

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Gu C, Liu S, Wang H, Dou H. Role of the thioredoxin interacting protein in diabetic nephropathy and the mechanism of regulating NOD-like receptor protein 3 inflammatory corpuscle. Int J Mol Med (2019) 43(6):2440–50. doi: 10.3892/ijmm.2019.4163

PubMed Abstract | CrossRef Full Text | Google Scholar

55. Pinto-Junior DC, Silva KS, Michalani ML, Yonamine CY, Esteves JV, Fabre NT, et al. Advanced glycation end products-induced insulin resistance involves repression of skeletal muscle GLUT4 expression. Sci Rep (2018) 8(1):8109. doi: 10.1038/s41598-018-26482-6

PubMed Abstract | CrossRef Full Text | Google Scholar

56. Chou CW, Hsieh YH, Ku SC, Shen WJ, Anuraga G, Khoa Ta HD, et al. Potential prognostic biomarkers of OSBPL family genes in patients with pancreatic ductal adenocarcinoma. Biomedicines (2021) 9(11):1601. doi: 10.3390/biomedicines9111601

PubMed Abstract | CrossRef Full Text | Google Scholar

57. Wright MB, Varona Santos J, Kemmer C, Maugeais C, Carralot JP, Roever S, et al. Compounds targeting OSBPL7 increase ABCA1-dependent cholesterol efflux preserving kidney function in two models of kidney disease. Nat Commun (2021) 12(1):4662. doi: 10.1038/s41467-021-24890-3

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Krishna S, Arrojo E Drigo R, Capitanio JS, Ramachandra R, Ellisman M, Hetzer MW. Identification of long-lived proteins in the mitochondria reveals increased stability of the electron transport chain. Dev Cell (2021) 56(21):2952–65. doi: 10.1016/j.devcel.2021.10.008

PubMed Abstract | CrossRef Full Text | Google Scholar

59. Wang X, Wang LT, Yu B. UBE2D1 and COX7C as potential biomarkers of diabetes-related sepsis. BioMed Res Int (2022) 2022:9463717. doi: 10.1155/2022/9463717

PubMed Abstract | CrossRef Full Text | Google Scholar

60. Zeng Z, Yu J, Yang Z, Du K, Chen Y, Zhou L. Investigation of M2 macrophage-related gene affecting patients prognosis and drug sensitivity in non-small cell lung cancer: evidence from bioinformatic and experiments. Front Oncol (2022) 12:1096449. doi: 10.3389/fonc.2022.1096449

PubMed Abstract | CrossRef Full Text | Google Scholar

61. Ahn EH, Kim DW, Shin MJ, Ryu EJ, Yong JI, Chung SY, et al. Tat-ATOX1 inhibits streptozotocin-induced cell death in pancreatic RINm5F cells and attenuates diabetes in a mouse model. Int J Mol Med (2016) 38(1):217–24. doi: 10.3892/ijmm.2016.2599

PubMed Abstract | CrossRef Full Text | Google Scholar

62. Jiang A, Gao H, Kelley MR, Qiao X. Inhibition of APE1/Ref-1 redox activity with APX3330 blocks retinal angiogenesis in vitro and in vivo. Vision Res (2011) 51(1):93–100. doi: 10.1016/j.visres.2010.10.008

PubMed Abstract | CrossRef Full Text | Google Scholar

63. Moreno-Gonzalez I, Edwards Iii G, Salvadores N, Shahnawaz M, Diaz-Espinoza R, Soto C. Molecular interaction between type 2 diabetes and alzheimer's disease through cross-seeding of protein misfolding. Mol Psychiatry (2017) 22(9):1327–34. doi: 10.1038/mp.2016.230

PubMed Abstract | CrossRef Full Text | Google Scholar

64. Wu Z, Chen A, Zhang G, Liu C, Yin S, Song R, et al. ALDH3B1 protects interfollicular epidermal cells against lipid peroxidation via the NRF2 pathway. Cell Stress Chaperones (2022) 27(6):703–15. doi: 10.1007/s12192-022-01306-9

PubMed Abstract | CrossRef Full Text | Google Scholar

65. Pei-Yuan Z, Yu-Wei L, Xiang-Nan Z, Song T, Rong Z, Xiao-Xiao H, et al. Overexpression of axl reverses endothelial cells dysfunction in high glucose and hypoxia. J Cell Biochem (2019) 120(7):11831–41. doi: 10.1002/jcb.28462

PubMed Abstract | CrossRef Full Text | Google Scholar

66. Yu N, Fang X, Zhao D, Mu Q, Zuo J, Ma Y, et al. Anti-diabetic effects of jiang tang xiao ke granule via PI3K/Akt signalling pathway in type 2 diabetes KKAy mice. PloS One (2017) 12(1):e0168980. doi: 10.1371/journal.pone.0168980

PubMed Abstract | CrossRef Full Text | Google Scholar

67. Pan X, Mota S, Zhang B. Circadian clock regulation on lipid metabolism and metabolic diseases. Adv Exp Med Biol (2020) 1276:53–66. doi: 10.1007/978-981-15-6082-8_5

PubMed Abstract | CrossRef Full Text | Google Scholar

68. Tian D, Xiang Y, Tang Y, Ge Z, Li Q, Zhang Y. Circ-ADAM9 targeting PTEN and ATG7 promotes autophagy and apoptosis of diabetic endothelial progenitor cells by sponging mir-20a-5p. Cell Death Dis (2020) 11(7):526. doi: 10.1038/s41419-020-02745-x

PubMed Abstract | CrossRef Full Text | Google Scholar

69. Anderson KA, Lin F, Ribar TJ, Stevens RD, Muehlbauer MJ, Newgard CB, et al. Deletion of CaMKK2 from the liver lowers blood glucose and improves whole-body glucose tolerance in the mouse. Mol Endocrinol (2012) 26(2):281–91. doi: 10.1210/me.2011-1299

PubMed Abstract | CrossRef Full Text | Google Scholar

70. Carmona-Rivera C, Simeonov DR, Cardillo ND, Gahl WA, Cadilla CL. A divalent interaction between HPS1 and HPS4 is required for the formation of the biogenesis of lysosome-related organelle complex-3 (BLOC-3). Biochim Biophys Acta (2013) 1833(3):468–78. doi: 10.1016/j.bbamcr.2012.10.019

PubMed Abstract | CrossRef Full Text | Google Scholar

71. Tang H, Huang X, Pang S. Regulation of the lysosome by sphingolipids: potential role in aging. J Biol Chem (2022) 298(7):102118. doi: 10.1016/j.jbc.2022.102118

PubMed Abstract | CrossRef Full Text | Google Scholar

72. Patel V, Bidault G, Chambers JE, Carobbio S, Everden AJT, Garcés C, et al. Inactivation of Ppp1r15a minimises weight gain and insulin resistance during caloric excess in female mice. Sci Rep (2019) 9(1):2903. doi: 10.1038/s41598-019-39562-y

PubMed Abstract | CrossRef Full Text | Google Scholar

73. Zhao Y, Li W, Zhang K, Xu M, Zou Y, Qiu X, et al. Revealing oxidative stress-related genes in osteoporosis and advanced structural biological study for novel natural material discovery regarding MAPKAPK2. Front Endocrinol (Lausanne) (2022) 13:1052721. doi: 10.3389/fendo.2022.1052721

PubMed Abstract | CrossRef Full Text | Google Scholar

74. Hatori Y, Lutsenko S. The role of copper chaperone Atox1 in coupling redox homeostasis to intracellular copper distribution. Antioxidants (Basel) (2016) 5(3):25. doi: 10.3390/antiox5030025

PubMed Abstract | CrossRef Full Text | Google Scholar

75. Hatori Y, Lutsenko S. An expanding range of functions for the copper chaperone/antioxidant protein Atox1. Antioxid Redox Signal (2013) 19(9):945–57. doi: 10.1089/ars.2012.5086

PubMed Abstract | CrossRef Full Text | Google Scholar

76. Hassan A, Sharma Kandel R, Mishra R, Gautam J, Alaref A, Jahan N. Diabetes mellitus and parkinson's disease: shared pathophysiological links and possible therapeutic implications. Cureus (2020) 12(8):e9853. doi: 10.7759/cureus.9853

PubMed Abstract | CrossRef Full Text | Google Scholar

77. Cheong JLY, de Pablo-Fernandez E, Foltynie T, Noyce AJ. The association between type 2 diabetes mellitus and parkinson's disease. J Parkinsons Dis (2020) 10(3):775–89. doi: 10.3233/JPD-191900

PubMed Abstract | CrossRef Full Text | Google Scholar

78. Chohan H, Senkevich K, Patel RK, Bestwick JP, Jacobs BM, Bandres Ciga S, et al. Type 2 diabetes as a determinant of parkinson's disease risk and progression. Mov Disord (2021) 36(6):1420–9. doi: 10.1002/mds.28551

PubMed Abstract | CrossRef Full Text | Google Scholar

79. Pugazhenthi S, Qin L, Reddy PH. Common neurodegenerative pathways in obesity, diabetes, and alzheimer's disease. Biochim Biophys Acta Mol Basis Dis (2017) 1863(5):1037–45. doi: 10.1016/j.bbadis.2016.04.017

PubMed Abstract | CrossRef Full Text | Google Scholar

80. Burillo J, Marqués P, Jiménez B, González-Blanco C, Benito M, Guillén C. Insulin resistance and diabetes mellitus in alzheimer's disease. Cells (2021) 10(5):1236. doi: 10.3390/cells10051236

PubMed Abstract | CrossRef Full Text | Google Scholar

81. Kreider RB, Kalman DS, Antonio J, Ziegenfuss TN, Wildman R, Collins R, et al. International society of sports nutrition position stand: safety and efficacy of creatine supplementation in exercise, sport, and medicine. J Int Soc Sports Nutr (2017) 14:18. doi: 10.1186/s12970-017-0173-z

PubMed Abstract | CrossRef Full Text | Google Scholar

82. Akhoundi M, Downing T, Votýpka J, Kuhls K, Lukeš J, Cannet A, et al. Leishmania infections: molecular targets and diagnosis. Mol Aspects Med (2017) 57:1–29. doi: 10.1016/j.mam.2016.11.012

PubMed Abstract | CrossRef Full Text | Google Scholar

83. Ashayeri Ahmadabad R, Mirzaasgari Z, Gorji A, Khaleghi Ghadiri M. Toll-like receptor signaling pathways: novel therapeutic targets for cerebrovascular disorders. Int J Mol Sci (2021) 22(11):6153. doi: 10.3390/ijms22116153

PubMed Abstract | CrossRef Full Text | Google Scholar

84. Yu GH, Li SF, Wei R, Jiang Z. Diabetes and colorectal cancer risk: clinical and therapeutic implications. J Diabetes Res (2022) 2022:1747326. doi: 10.1155/2022/1747326

PubMed Abstract | CrossRef Full Text | Google Scholar

85. Dissanayake WC, Sorrenson B, Shepherd PR. The role of adherens junction proteins in the regulation of insulin secretion. Biosci Rep (2018) 38(2):BSR20170989. doi: 10.1042/BSR20170989

PubMed Abstract | CrossRef Full Text | Google Scholar

86. Zikherman J, Au-Yeung B. The role of T cell receptor signaling thresholds in guiding T cell fate decisions. Curr Opin Immunol (2015) 33:43–8. doi: 10.1016/j.coi.2015.01.012

PubMed Abstract | CrossRef Full Text | Google Scholar

87. Nolfi-Donegan D, Braganza A, Shiva S. Mitochondrial electron transport chain: Oxidative phosphorylation, oxidant production, and methods of measurement. Redox Biol (2020) 37:101674. doi: 10.1016/j.redox.2020.101674

PubMed Abstract | CrossRef Full Text | Google Scholar

88. Kolwicz SC Jr, Purohit S, Tian R. Cardiac metabolism and its interactions with contraction, growth, and survival of cardiomyocytes. Circ Res (2013) 113(5):603–16. doi: 10.1161/CIRCRESAHA.113.302095

PubMed Abstract | CrossRef Full Text | Google Scholar

89. Zheng X, Narayanan S, Xu C, Eliasson Angelstig S, Grünler J, Zhao A, et al. Repression of hypoxia-inducible factor-1 contributes to increased mitochondrial reactive oxygen species production in diabetes. Elife (2022) 11:e70714. doi: 10.7554/eLife.70714

PubMed Abstract | CrossRef Full Text | Google Scholar

90. Leenders F, Groen N, de Graaf N, Engelse MA, Rabelink TJ, de Koning EJP, et al. Oxidative stress leads to β-cell dysfunction through loss of β-cell identity. Front Immunol (2021) 12:690379. doi: 10.3389/fimmu.2021.690379

PubMed Abstract | CrossRef Full Text | Google Scholar

91. Walton EL. Oxidative stress and diabetes: glucose response in the cROSsfire. BioMed J (2017) 40(5):241–4. doi: 10.1016/j.bj.2017.10.001

PubMed Abstract | CrossRef Full Text | Google Scholar

92. He L, He T, Farrar S, Ji L, Liu T, Ma X. Antioxidants maintain cellular redox homeostasis by elimination of reactive oxygen species. Cell Physiol Biochem (2017) 44(2):532–53. doi: 10.1159/000485089

PubMed Abstract | CrossRef Full Text | Google Scholar

93. Ray PD, Huang BW, Tsuji Y. Reactive oxygen species (ROS) homeostasis and redox regulation in cellular signaling. Cell Signal (2012) 24(5):981–90. doi: 10.1016/j.cellsig.2012.01.008

PubMed Abstract | CrossRef Full Text | Google Scholar

94. Simaan H, Lev S, Horwitz BA. Oxidant-sensing pathways in the responses of fungal pathogens to chemical stress signals. Front Microbiol (2019) 10:567. doi: 10.3389/fmicb.2019.00567

PubMed Abstract | CrossRef Full Text | Google Scholar

95. Levonen AL, Hill BG, Kansanen E, Zhang J, Darley-Usmar VM. Redox regulation of antioxidants, autophagy, and the response to stress: implications for electrophile therapeutics. Free Radic Biol Med (2014) 71:196–207. doi: 10.1016/j.freeradbiomed.2014.03.025

PubMed Abstract | CrossRef Full Text | Google Scholar

96. Orang AV, Petersen J, McKinnon RA, Michael MZ. Micromanaging aerobic respiration and glycolysis in cancer cells. Mol Metab (2019) 23:98–126. doi: 10.1016/j.molmet.2019.01.014

PubMed Abstract | CrossRef Full Text | Google Scholar

97. Kasai S, Shimizu S, Tatara Y, Mimura J, Itoh K. Regulation of Nrf2 by mitochondrial reactive oxygen species in physiology and pathology. Biomolecules (2020) 10(2):320. doi: 10.3390/biom10020320

PubMed Abstract | CrossRef Full Text | Google Scholar

98. Toyoda Y, Saitoh S. Adaptive regulation of glucose transport, glycolysis and respiration for cell proliferation. Biomol Concepts (2015) 6(5-6):423–30. doi: 10.1515/bmc-2015-0018

PubMed Abstract | CrossRef Full Text | Google Scholar

99. Rabbani N, Thornalley PJ. Hexokinase-2 glycolytic overload in diabetes and ischemia-reperfusion injury. Trends Endocrinol Metab (2019) 30(7):419–31. doi: 10.1016/j.tem.2019.04.011

PubMed Abstract | CrossRef Full Text | Google Scholar

100. Klinge CM. Estrogenic control of mitochondrial function. Redox Biol (2020) 31:101435. doi: 10.1016/j.redox.2020.101435

PubMed Abstract | CrossRef Full Text | Google Scholar

101. Zhang J, Wang X, Vikash V, Ye Q, Wu D, Liu Y, et al. ROS and ROS-mediated cellular signaling. Oxid Med Cell Longev (2016) 2016:4350965. doi: 10.1155/2016/4350965

PubMed Abstract | CrossRef Full Text | Google Scholar

102. Xu L, Li Y, Yin L, Qi Y, Sun H, Sun P, et al. miR-125a-5p ameliorates hepatic glycolipid metabolism disorder in type 2 diabetes mellitus through targeting of STAT3. Theranostics (2018) 8(20):5593–609. doi: 10.7150/thno.27425

PubMed Abstract | CrossRef Full Text | Google Scholar

103. Coulthard LR, White DE, Jones DL, McDermott MF, Burchill SA. p38(MAPK): stress responses from molecular mechanisms to therapeutics. Trends Mol Med (2009) 15(8):369–79. doi: 10.1016/j.molmed.2009.06.005

PubMed Abstract | CrossRef Full Text | Google Scholar

104. Popa A, Georgescu M, Popa SG, Nica AE, Georgescu EF. New insights in the molecular pathways linking obesity, type 2 diabetes and cancer. Rom J Morphol Embryol (2019) 60(4):1115–25.

PubMed Abstract | Google Scholar

105. Hüttemann M, Lee I, Samavati L, Yu H, Doan JW. Regulation of mitochondrial oxidative phosphorylation through cell signaling. Biochim Biophys Acta (2007) 1773(12):1701–20. doi: 10.1016/j.bbamcr.2007.10.001

PubMed Abstract | CrossRef Full Text | Google Scholar

106. Andrae J, Gallini R, Betsholtz C. Role of platelet-derived growth factors in physiology and medicine. Genes Dev (2008) 22(10):1276–312. doi: 10.1101/gad.1653708

PubMed Abstract | CrossRef Full Text | Google Scholar

107. Drouin M, Saenz J, Chiffoleau E. C-type lectin-like receptors: head or tail in cell death immunity. Front Immunol (2020) 11:251. doi: 10.3389/fimmu.2020.00251

PubMed Abstract | CrossRef Full Text | Google Scholar

108. Ivanova EA, Orekhov AN. Monocyte activation in immunopathology: cellular test for development of diagnostics and therapy. J Immunol Res (2016) 2016:4789279. doi: 10.1155/2016/4789279

PubMed Abstract | CrossRef Full Text | Google Scholar

109. Coulthard MG, Morgan M, Woodruff TM, Arumugam TV, Taylor SM, Carpenter TC, et al. Eph/Ephrin signaling in injury and inflammation. Am J Pathol (2012) 181(5):1493–503. doi: 10.1016/j.ajpath.2012.06.043

PubMed Abstract | CrossRef Full Text | Google Scholar

110. Boekema EJ, Braun HP. Supramolecular structure of the mitochondrial oxidative phosphorylation system. J Biol Chem (2007) 282(1):1–4. doi: 10.1074/jbc.R600031200

PubMed Abstract | CrossRef Full Text | Google Scholar

111. Picca A, Guerra F, Calvani R, Romano R, Coelho-Júnior HJ, Bucci C, et al. Mitochondrial dysfunction, protein misfolding and neuroinflammation in parkinson's disease: roads to biomarker discovery. Biomolecules (2021) 11(10):1508. doi: 10.3390/biom11101508

PubMed Abstract | CrossRef Full Text | Google Scholar

112. Ejma M, Madetko N, Brzecka A, Guranski K, Alster P, Misiuk-Hojło M, et al. The links between parkinson's disease and cancer. Biomedicines (2020) 8(10):416. doi: 10.3390/biomedicines8100416

PubMed Abstract | CrossRef Full Text | Google Scholar

113. Singh N, Baby D, Rajguru JP, Patil PB, Thakkannavar SS, Pujari VB. Inflammation and cancer. Ann Afr Med (2019) 18(3):121–6. doi: 10.4103/aam.aam_56_18

PubMed Abstract | CrossRef Full Text | Google Scholar

114. Daryabor G, Atashzar MR, Kabelitz D, Meri S, Kalantar K. The effects of type 2 diabetes mellitus on organ metabolism and the immune system. Front Immunol (2020) 11:1582. doi: 10.3389/fimmu.2020.01582

PubMed Abstract | CrossRef Full Text | Google Scholar

115. Huang R, Zhou PK. DNA Damage repair: historical perspectives, mechanistic pathways and clinical translation for targeted cancer therapy. Signal Transduct Target Ther (2021) 6(1):254. doi: 10.1038/s41392-021-00648-7

PubMed Abstract | CrossRef Full Text | Google Scholar

116. Ntambi JM, Miyazaki M, Stoehr JP, Lan H, Kendziorski CM, Yandell BS, et al. Loss of stearoyl-CoA desaturase-1 function protects mice against adiposity. Proc Natl Acad Sci USA (2002) 99(17):11482–6. doi: 10.1073/pnas.132384699

PubMed Abstract | CrossRef Full Text | Google Scholar

117. Yuan X, Hu S, Li L, Liu H, He H, Wang J. Metabolomic analysis of SCD during goose follicular development: implications for lipid metabolism. Genes (Basel) (2020) 11(9):1001. doi: 10.3390/genes11091001

PubMed Abstract | CrossRef Full Text | Google Scholar

118. Yuan X, Abdul-Rahman I, Hu S, Li L, He H, Xia L, et al. Mechanism of SCD participation in lipid droplet-mediated steroidogenesis in goose granulosa cells. Genes (Basel) (2022) 13(9):1516. doi: 10.3390/genes13091516

PubMed Abstract | CrossRef Full Text | Google Scholar

119. Tang B, Qiu J, Hu S, Li L, Wang J. Role of stearyl-coenzyme a desaturase 1 in mediating the effects of palmitic acid on endoplasmic reticulum stress, inflammation, and apoptosis in goose primary hepatocytes. Anim Biosci (2021) 34(7):1210–20. doi: 10.5713/ajas.20.0444

PubMed Abstract | CrossRef Full Text | Google Scholar

120. Valentine D, Teerlink CC, Farnham JM, Rowe K, Kaddas H, Tschanz J, et al. Comorbidity and cancer disease rates among those at high-risk for alzheimer's disease: a population database analysis. Int J Environ Res Public Health (2022) 19(24):16419. doi: 10.3390/ijerph192416419

PubMed Abstract | CrossRef Full Text | Google Scholar

121. Kesler SR, Rao V, Ray WJ, Rao A. Alzheimer's disease neuroimaging initiative. probability of alzheimer's disease in breast cancer survivors based on gray-matter structural network efficiency. Alzheimers Dement (Amst) (2017) 9:67–75. doi: 10.1016/j.dadm.2017.10.002

PubMed Abstract | CrossRef Full Text | Google Scholar

122. Kroemer G, Pouyssegur J. Tumor cell metabolism: cancer's achilles' heel. Cancer Cell (2008) 13(6):472–82. doi: 10.1016/j.ccr.2008.05.005

PubMed Abstract | CrossRef Full Text | Google Scholar

123. Ojha R, Tantray I, Rimal S, Mitra S, Cheshier S, Lu B. Regulation of reverse electron transfer at mitochondrial complex I by unconventional notch action in cancer stem cells. Dev Cell (2022) 57(2):260–276.e9. doi: 10.1016/j.devcel.2021.12.020

PubMed Abstract | CrossRef Full Text | Google Scholar

124. Arteaga CL, Engelman JA. ERBB receptors: from oncogene discovery to basic science to mechanism-based cancer therapeutics. Cancer Cell (2014) 25(3):282–303. doi: 10.1016/j.ccr.2014.02.025

PubMed Abstract | CrossRef Full Text | Google Scholar

125. Dressing GE, Goldberg JE, Charles NJ, Schwertfeger KL, Lange CA. Membrane progesterone receptor expression in mammalian tissues: a review of regulation and physiological implications. Steroids (2011) 76(1-2):11–7. doi: 10.1016/j.steroids.2010.09.006

PubMed Abstract | CrossRef Full Text | Google Scholar

126. Goral V. Pancreatic cancer: pathogenesis and diagnosis. Asian Pac J Cancer Prev (2015) 16(14):5619–24. doi: 10.7314/apjcp.2015.16.14.5619

PubMed Abstract | CrossRef Full Text | Google Scholar

127. Ko HJ, Chang SY. Regulation of intestinal immune system by dendritic cells. Immune Netw (2015) 15(1):1–8. doi: 10.4110/in.2015.15.1.1

PubMed Abstract | CrossRef Full Text | Google Scholar

128. Zsindely N, Siági F, Bodai L. DNA Methylation in huntington's disease. Int J Mol Sci (2021) 22(23):12736. doi: 10.3390/ijms222312736

PubMed Abstract | CrossRef Full Text | Google Scholar

129. LeBleu VS, O'Connell JT, Gonzalez Herrera KN, Wikman H, Pantel K, Haigis MC, et al. PGC-1α mediates mitochondrial biogenesis and oxidative phosphorylation in cancer cells to promote metastasis. Nat Cell Biol (2014) 16(10):992–1003. doi: 10.1038/ncb3039

PubMed Abstract | CrossRef Full Text | Google Scholar

130. Gray RE, Harris GT. Renal cell carcinoma: diagnosis and management. Am Fam Physician. (2019) 99(3):179–84.

PubMed Abstract | Google Scholar

131. Davis LE, Shalin SC, Tackett AJ. Current state of melanoma diagnosis and treatment. Cancer Biol Ther (2019) 20(11):1366–79. doi: 10.1080/15384047.2019.1640032

PubMed Abstract | CrossRef Full Text | Google Scholar

132. Kader AK. Bladder cancer. Sci World J (2011) 11:2565–6. doi: 10.1100/2011/251920

CrossRef Full Text | Google Scholar

133. Venhuizen JH, Jacobs FJC, Span PN, Zegers MM. P120 and e-cadherin: double-edged swords in tumor metastasis. Semin Cancer Biol (2020) 60:107–20. doi: 10.1016/j.semcancer.2019.07.020

PubMed Abstract | CrossRef Full Text | Google Scholar

134. Ludwig K, Kornblum HI. Molecular markers in glioma. J Neurooncol (2017) 134(3):505–12. doi: 10.1007/s11060-017-2379-y

PubMed Abstract | CrossRef Full Text | Google Scholar

135. Totland MZ, Rasmussen NL, Knudsen LM, Leithe E. Regulation of gap junction intercellular communication by connexin ubiquitination: physiological and pathophysiological implications. Cell Mol Life Sci (2020) 77(4):573–91. doi: 10.1007/s00018-019-03285-0

PubMed Abstract | CrossRef Full Text | Google Scholar

136. Buono R, Longo VD. Starvation, stress resistance, and cancer. Trends Endocrinol Metab (2018) 29(4):271–80. doi: 10.1016/j.tem.2018.01.008

PubMed Abstract | CrossRef Full Text | Google Scholar

137. Yu L, Lu M, Jia D, Ma J, Ben-Jacob E, Levine H, et al. Modeling the genetic regulation of cancer metabolism: interplay between glycolysis and oxidative phosphorylation. Cancer Res (2017) 77(7):1564–74. doi: 10.1158/0008-5472.CAN-16-2074

PubMed Abstract | CrossRef Full Text | Google Scholar

138. Owusu BY, Galemmo R, Janetka J, Klampfer L. Hepatocyte growth factor, a key tumor-promoting factor in the tumor microenvironment. Cancers (Basel) (2017) 9(4):35. doi: 10.3390/cancers9040035

PubMed Abstract | CrossRef Full Text | Google Scholar

139. Hartwig A. Cadmium and cancer. Met Ions Life Sci (2013) 11:491–507. doi: 10.1007/978-94-007-5179-8_15

PubMed Abstract | CrossRef Full Text | Google Scholar

140. Song Y, Xu Y, Pan C, Yan L, Wang ZW, Zhu X. The emerging role of SPOP protein in tumorigenesis and cancer therapy. Mol Cancer (2020) 19(1):2. doi: 10.1186/s12943-019-1124-x

PubMed Abstract | CrossRef Full Text | Google Scholar

141. Vilema-Enríquez G, Arroyo A, Grijalva M, Amador-Zafra RI, Camacho J. Molecular and cellular effects of hydrogen peroxide on human lung cancer cells: potential therapeutic implications. Oxid Med Cell Longev (2016) 2016:1908164. doi: 10.1155/2016/1908164

PubMed Abstract | CrossRef Full Text | Google Scholar

142. Lam T, Aguirre-Ghiso JA, Geller MA, Aksan A, Azarin SM. Immobilization rapidly selects for chemoresistant ovarian cancer cells with enhanced ability to enter dormancy. Biotechnol Bioeng (2020) 117(10):3066–80. doi: 10.1002/bit.27479

PubMed Abstract | CrossRef Full Text | Google Scholar

143. Gao X, Yang Y, Wang J, Zhang L, Sun C, Wang Y, et al. Inhibition of mitochondria NADH-ubiquinone oxidoreductase (complex I) sensitizes the radioresistant glioma U87MG cells to radiation. BioMed Pharmacother (2020), 129:110460. doi: 10.1016/j.biopha.2020.110460

CrossRef Full Text | Google Scholar

144. Glorieux C, Calderon PB. Catalase, a remarkable enzyme: targeting the oldest antioxidant enzyme to find a new cancer treatment approach. Biol Chem (2017) 398(10):1095–108. doi: 10.1515/hsz-2017-0131

PubMed Abstract | CrossRef Full Text | Google Scholar

145. Raimondi V, Ciccarese F, Ciminale V. Oncogenic pathways and the electron transport chain: a dangeROS liaison. Br J Cancer (2020) 122(2):168–81. doi: 10.1038/s41416-019-0651-y

PubMed Abstract | CrossRef Full Text | Google Scholar

146. Thorne JL, Campbell MJ. Nuclear receptors and the warburg effect in cancer. Int J Cancer (2015) 137(7):1519–27. doi: 10.1002/ijc.29012

PubMed Abstract | CrossRef Full Text | Google Scholar

147. Ginckels P, Holvoet P. Oxidative stress and inflammation in cardiovascular diseases and cancer: role of non-coding RNAs. Yale J Biol Med (2022) 95(1):129–52.

PubMed Abstract | Google Scholar

148. Aggarwal V, Tuli HS, Varol A, Thakral F, Yerer MB, Sak K, et al. Role of reactive oxygen species in cancer progression: molecular mechanisms and recent advancements. Biomolecules (2019) 9(11):735. doi: 10.3390/biom9110735

PubMed Abstract | CrossRef Full Text | Google Scholar

149. Schwarz EC, Qu B, Hoth M. Calcium, cancer and killing: the role of calcium in killing cancer cells by cytotoxic T lymphocytes and natural killer cells. Biochim Biophys Acta (2013) 1833(7):1603–11. doi: 10.1016/j.bbamcr.2012.11.016

PubMed Abstract | CrossRef Full Text | Google Scholar

150. Cautain B, Hill R, de Pedro N, Link W. Components and regulation of nuclear transport processes. FEBS J (2015) 282(3):445–62. doi: 10.1111/febs.13163

PubMed Abstract | CrossRef Full Text | Google Scholar

151. Fogg VC, Lanning NJ, Mackeigan JP. Mitochondria in cancer: at the crossroads of life and death. Chin J Cancer (2011) 30(8):526–39. doi: 10.5732/cjc.011.10018

PubMed Abstract | CrossRef Full Text | Google Scholar

152. Law ML, Metzger JM. Cardiac myocyte intrinsic contractility and calcium handling deficits underlie heart organ dysfunction in murine cancer cachexia. Sci Rep (2021) 11(1):23627. doi: 10.1038/s41598-021-02688-z

PubMed Abstract | CrossRef Full Text | Google Scholar

153. Amelio I, Cutruzzolá F, Antonov A, Agostini M, Melino G. Serine and glycine metabolism in cancer. Trends Biochem Sci (2014) 39(4):191–8. doi: 10.1016/j.tibs.2014.02.004

PubMed Abstract | CrossRef Full Text | Google Scholar

154. Liu J, Wu Z, Han D, Wei C, Liang Y, Jiang T, et al. Mesencephalic astrocyte-derived neurotrophic factor inhibits liver cancer through small ubiquitin-related modifier (SUMO)ylation-related suppression of NF-κB/Snail signaling pathway and epithelial-mesenchymal transition. Hepatology (2020) 71(4):1262–78. doi: 10.1002/hep.30917

PubMed Abstract | CrossRef Full Text | Google Scholar

155. Schuldt M, Pei J, Harakalova M, Dorsch LM, Schlossarek S, Mokry M, et al. Proteomic and functional studies reveal detyrosinated tubulin as treatment target in sarcomere mutation-induced hypertrophic cardiomyopathy. Circ Heart Fail (2021) 14(1):e007022. doi: 10.1161/CIRCHEARTFAILURE.120.007022

PubMed Abstract | CrossRef Full Text | Google Scholar

156. Faas M, Ipseiz N, Ackermann J, Culemann S, Grüneboom A, Schröder F, et al. IL-33-induced metabolic reprogramming controls the differentiation of alternatively activated macrophages and the resolution of inflammation. Immunity (2021) 54(11):2531–2546.e5. doi: 10.1016/j.immuni.2021.09.010

PubMed Abstract | CrossRef Full Text | Google Scholar

157. Ward MH, Jones RR, Brender JD, de Kok TM, Weyer PJ, Nolan BT, et al. Drinking water nitrate and human health: an updated review. Int J Environ Res Public Health (2018) 15(7):1557. doi: 10.3390/ijerph15071557

PubMed Abstract | CrossRef Full Text | Google Scholar

158. Vultaggio-Poma V, Sarti AC, Di Virgilio F. Extracellular ATP: a feasible target for cancer therapy. Cells (2020) 9(11):2496. doi: 10.3390/cells9112496

PubMed Abstract | CrossRef Full Text | Google Scholar

159. Martínez-Reyes I, Cardona LR, Kong H, Vasan K, McElroy GS, Werner M, et al. Mitochondrial ubiquinol oxidation is necessary for tumour growth. Nature (2020) 585(7824):288–92. doi: 10.1038/s41586-020-2475-6

PubMed Abstract | CrossRef Full Text | Google Scholar

160. Radin DP, Tsirka SE. Interactions between tumor cells, neurons, and microglia in the glioma microenvironment. Int J Mol Sci (2020) 21(22):8476. doi: 10.3390/ijms21228476

PubMed Abstract | CrossRef Full Text | Google Scholar

161. Koike H, Iwasawa K, Ouchi R, Maezawa M, Giesbrecht K, Saiki N, et al. Modelling human hepato-biliary-pancreatic organogenesis from the foregut-midgut boundary. Nature (2019) 574(7776):112–6. doi: 10.1038/s41586-019-1598-0

PubMed Abstract | CrossRef Full Text | Google Scholar

162. Porporato PE, Filigheddu N, Pedro JMB, Kroemer G, Galluzzi L. Mitochondrial metabolism and cancer. Cell Res (2018) 28(3):265–80. doi: 10.1038/cr.2017.155

PubMed Abstract | CrossRef Full Text | Google Scholar

163. Zong WX, Rabinowitz JD, White E. Mitochondria and cancer. Mol Cell (2016) 61(5):667–76. doi: 10.1016/j.molcel.2016.02.011

PubMed Abstract | CrossRef Full Text | Google Scholar

164. Grimm MO, Mett J, Grimm HS, Hartmann T, Function APP. And lipids: a bidirectional link. Front Mol Neurosci (2017) 10:63. doi: 10.3389/fnmol.2017.00063

PubMed Abstract | CrossRef Full Text | Google Scholar

165. Wang J, Wang X, Guo Y, Ye L, Li D, Hu A, et al. Therapeutic targeting of SPIB/SPI1-facilitated interplay of cancer cells and neutrophils inhibits aerobic glycolysis and cancer progression. Clin Transl Med (2021) 11(11):e588. doi: 10.1002/ctm2.588

PubMed Abstract | CrossRef Full Text | Google Scholar

166. Sin O, Nollen EA. Regulation of protein homeostasis in neurodegenerative diseases: the role of coding and non-coding genes. Cell Mol Life Sci (2015) 72(21):4027–47. doi: 10.1007/s00018-015-1985-0

PubMed Abstract | CrossRef Full Text | Google Scholar

167. Press M, Jung T, König J, Grune T, Höhn A. Protein aggregates and proteostasis in aging: amylin and β-cell function. Mech Ageing Dev (2019) 177:46–54. doi: 10.1016/j.mad.2018.03.010

PubMed Abstract | CrossRef Full Text | Google Scholar

168. Archuleta TL, Lemieux AM, Saengsirisuwan V, Teachey MK, Lindborg KA, Kim JS, et al. Oxidant stress-induced loss of IRS-1 and IRS-2 proteins in rat skeletal muscle: role of p38 MAPK. Free Radic Biol Med (2009) 47(10):1486–93. doi: 10.1016/j.freeradbiomed.2009.08.014

PubMed Abstract | CrossRef Full Text | Google Scholar

169. Berdichevsky A, Guarente L, Bose A. Acute oxidative stress can reverse insulin resistance by inactivation of cytoplasmic JNK. J Biol Chem (2010) 285(28):21581–9. doi: 10.1074/jbc.M109.093633

PubMed Abstract | CrossRef Full Text | Google Scholar

170. Theocharidis G, Thomas BE, Sarkar D, Mumme HL, Pilcher WJR, Dwivedi B, et al. Single cell transcriptomic landscape of diabetic foot ulcers. Nat Commun (2022) 13(1):181. doi: 10.1038/s41467-021-27801-8

PubMed Abstract | CrossRef Full Text | Google Scholar

171. Song Y, He C, Jiang Y, Yang M, Xu Z, Yuan L, et al. Bulk and single-cell transcriptome analyses of islet tissue unravel gene signatures associated with pyroptosis and immune infiltration in type 2 diabetes. Front Endocrinol (Lausanne) (2023) 14:1132194. doi: 10.3389/fendo.2023.1132194

PubMed Abstract | CrossRef Full Text | Google Scholar

172. Wilson PC, Wu H, Kirita Y, Uchimura K, Ledru N, Rennke HG, et al. The single-cell transcriptomic landscape of early human diabetic nephropathy. Proc Natl Acad Sci USA (2019) 116(39):19619–25. doi: 10.1073/pnas.1908706116

PubMed Abstract | CrossRef Full Text | Google Scholar

173. Van Drie JH. Protein folding, protein homeostasis, and cancer. Chin J Cancer (2011) 30(2):124–37. doi: 10.5732/cjc.010.10162

PubMed Abstract | CrossRef Full Text | Google Scholar

174. Martínez de Toda I, Ceprián N, Díaz-Del Cerro E, de la Fuente M. The role of immune cells in oxi-Inflamm-Aging. Cells (2021) 10(11):2974. doi: 10.3390/cells10112974

PubMed Abstract | CrossRef Full Text | Google Scholar

175. Luc K, Schramm-Luc A, Guzik TJ, Mikolajczyk TP. Oxidative stress and inflammatory markers in prediabetes and diabetes. J Physiol Pharmacol (2019) 70(6):809–24. doi: 10.26402/jpp.2019.6.01

CrossRef Full Text | Google Scholar

176. Wu D, Bi X, Li P, Xu D, Qiu J, Li K, et al. Enhanced insulin-regulated phagocytic activities support extreme health span and longevity in multiple populations. Aging Cell (2023) 8:e13810. doi: 10.1111/acel.13810

CrossRef Full Text | Google Scholar

177. Lananna BV, Musiek ES. The wrinkling of time: aging, inflammation, oxidative stress, and the circadian clock in neurodegeneration. Neurobiol Dis (2020) 139:104832. doi: 10.1016/j.nbd.2020.104832

PubMed Abstract | CrossRef Full Text | Google Scholar

178. Saoudaoui S, Bernard M, Cardin GB, Malaquin N, Christopoulos A, Rodier F. mTOR as a senescence manipulation target: a forked road. Adv Cancer Res (2021) 150:335–63. doi: 10.1016/bs.acr.2021.02.002

PubMed Abstract | CrossRef Full Text | Google Scholar

179. Palmer AK, Gustafson B, Kirkland JL, Smith U. Cellular senescence: at the nexus between ageing and diabetes. Diabetologia (2019) 62(10):1835–41. doi: 10.1007/s00125-019-4934-x

PubMed Abstract | CrossRef Full Text | Google Scholar

180. Gao W, Liu JL, Lu X, Yang Q. Epigenetic regulation of energy metabolism in obesity. J Mol Cell Biol (2021) 13(7):480–99. doi: 10.1093/jmcb/mjab043

PubMed Abstract | CrossRef Full Text | Google Scholar

181. Burgess S, Smith GD, Davie NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing mendelian randomization investigations. version 2. Wellcome Open Res (2019) 4:186. doi: 10.12688/wellcomeopenres.15555.1

PubMed Abstract | CrossRef Full Text | Google Scholar

182. Burgess S, Foley CN, Allara E, Staley JR, Howson JMM, et al. A robust and efficient method for mendelian randomization with hundreds of genetic variants. Nat Commun (2020) 11:376. doi: 10.1038/s41467-019-14156-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: oxidative stress, type 2 diabetes mellitus, energy metabolism, aging, pan-cancer analysis

Citation: Chen Y, Yao L, Zhao S, Xu M, Ren S, Xie L, Liu L and Wang Y (2023) The oxidative aging model integrated various risk factors in type 2 diabetes mellitus at system level. Front. Endocrinol. 14:1196293. doi: 10.3389/fendo.2023.1196293

Received: 29 March 2023; Accepted: 10 May 2023;
Published: 24 May 2023.

Edited by:

Prem P. Kushwaha, Case Western Reserve University, United States

Reviewed by:

Alpna Tyagi, University of Colorado Anschutz Medical Campus, United States
Raushan Kumar, Allahabad University, India

Copyright © 2023 Chen, Yao, Zhao, Xu, Ren, Xie, Liu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Lu Xie, xielu@sibpt.com; Lei Liu, liulei@fudan.edu.cn; Yin Wang, chinawangyin@foxmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.