Chemokines in Prediabetes and Type 2 Diabetes: A Meta-Analysis

Abstract

Background:

A growing number of studies found inconsistent results on the role of chemokines in the progression of type 2 diabetes (T2DM) and prediabetes (PDM). The purpose of this meta-analysis was to summarize the results of previous studies on the association between the chemokines system and T2DM/PDM.

Methods:

We searched in the databases, PubMed, Web of Science, Embase and Cochrane Library, for eligible studies published not later than March 1, 2020. Data extraction was performed independently by 2 reviewers, on a standardized, prepiloted form. Group differences in chemokines concentrations were summarized using the standardized mean difference (SMD) with a 95% confidence interval (CI), calculated by performing a meta-analysis using the random-effects model.

Results:

We identified 98 relevant studies that investigated the association between 32 different chemokines and T2DM/PDM. Altogether, these studies involved 14,708 patients and 14,574 controls. Results showed that the concentrations of CCL1, CCL2, CCL4, CCL5, CCL11, CXCL8, CXCL10 and CX3CL1 in the T2DM patients were significantly higher than that in the controls, while no difference in these concentrations was found between the PDM patients and controls.

Conclusion:

Progression of T2DM may be associated with elevated concentrations of chemokines.

Meta-Analysis Registration:

PROSPERO, identifier CRD42019148305.

Background

With the growing global prevalence of obesity, the incidence of type 2 diabetes (T2DM) continues to increase and this has become one of the most important public health problems worldwide (1). T2DM cannot be completely cured, which seriously affects the quality of life of patients (2). Currently, research has shown that targeted anti-inflammatory drugs have great potential in improving insulin sensitivity and B-cell function (3). Meanwhile, a newly published meta-analysis showed that T2DM risk was closely related to the increased levels of inflammatory cytokines such as Tumor necrosis factor-α (TNF-α), Interleukin-1 beta (IL-1β), Interleukin-6 (IL-6), Interleukin-18 (IL-18), and C-reactive protein (CRP) (4). Also, accumulating evidence support an important hypothesis that there may be a complex interaction between chronic inflammation and the progression of diabetes (5, 6).

Furthermore, studies have shown that in addition to these inflammatory markers, chemokines have become key signaling molecules regulating the pathophysiological process of T2DM (7). Chemokines are the largest family of cytokines composed of four subfamilies (C, CC, CXC, and CX3C) (8). Thus, chemokines have been shown to function as signaling molecules in the inflammatory response, which can activate various pro-inflammatory mediators and induce various inflammatory factors (8, 9). These inflammatory factors stimulate the activation of cytokine signaling proteins, which eventually block the activation of insulin signaling receptors in pancreatic cells, and further induce insulin resistance (IR); hence participate in the progression of prediabetes (PDM) and T2DM (10).

The names of chemokines are not uniform at present. For example, there are about 50 chemokines acting on 23 discrete receptors, and studies on different chemokines in PDM and T2DM patients often produce inconsistent results (11, 12). It is unclear which chemokines vary in concentrations in PDM and T2DM patients to be considered as potential new biomarkers.

So far, there has not been a systematic review/meta-analysis on the role of chemokines in the progression of PDM and T2DM. Noteworthy, it is difficult to obtain statistically stable results from a single study. Thus, meta-analysis provides a gold standard method for aggregating data from several similar studies, and helps to improve statistical precision to achieve stable results. Therefore, the purpose of this study was to compare the chemokines concentrations between patients with PDM or T2DM and the control group by means of a meta-analysis, using as many variants of historical and current chemokines as possible retrieved through a wide range of search terms. The illustration of signal transduction pathway of chemokines in the pathophysiological process of PDM and T2DM could provide new targets and strategies for the prevention, diagnosis, and treatment of PDM and T2DM.

Methods

Search Strategy and Selection Criteria

The protocol of this systematic review has been registered in PROSPERO database (https://www.crd.york.ac.uk/PROSPERO), CRD42019148305. Also, the study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook (13).

Due to the inconsistent naming format of chemokines in published articles, the search strategy involved as many variant nomenclatures of chemokines as possible. According to the purpose of this study, two independent reviewers (AL and AK) selected relevant articles. In this regard, the electronic databases of Embase, Web of Science, Cochrane Library, and PubMed were searched for relevant articles published not later than March 1, 2020. The complete search strategy is shown in Appendix 1. Truncation, wildcards and boolean operators were used to allow for the retrieval of as many variant nomenclatures of chemokines as possible. Experienced librarians designed the search algorithm and customized it to meet the search requirements of each of the preceding electronic databases.

The following were inclusion criteria for eligible studies (1): the study design was longitudinal, cross-sectional or case-control; (2) the study reported the method for diagnosing PDM or T2DM; and (3) the study reported a mean chemokine concentration and its standard deviation (SD), or these could be obtained from the corresponding author. The exclusion criteria were: (1) letters, reviews, case reports, comments and non-human studies; (2) studies reported PDM or T2DM in combination with other diseases; (3) PDM and T2DM patients had the chemokines pharmacologically challenged before their measurements.

Data Extraction

Two independent reviewers (SW and AK) removed the duplicates using EndNote (version X9.1) and entered the eligible studies into a database constructed by EpiData (version 3.0) (14, 15). In this database, customized data extraction Excel spreadsheets were used to extract the data for this study (16). Differences between the two independent reviewers were resolved by involving a third reviewer (AL). Therefore, the following data were extracted from the eligible studies:(1) the first author’s last name and year of publication; (2) the country where the research was conducted; (3) type of control group; (4) PDM and T2DM patients characteristics such as duration of diabetes (years), waist circumference, waist to hip ratio, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose/fasting blood glucose (FPG), 2h postprandial blood glucose, Body Mass Index (BMI), age and gender of patients; (5) chemokine sample detection method and storage temperature; (6) sample material of chemokine, mean and standard deviation of chemokine concentration, blood biochemical indexes and other sample characteristics. Finally, the Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the risk of bias and quality of eligible studies (17).

Statistical Analysis

All statistical analyses were performed using the package meta in R (version 3.5.0) (18). Noteworthy, different chemokine assessment methods were used for different studies. Therefore, to be conservative, the random-effects model was used to determine the standardized mean difference (SMD) and 95% confidence interval (CI) for continuous data (19). Using the restricted maximum-likelihood estimator, the SMD was calculated using the Cohen’s d method to synthesize the effect size across studies (20, 21). Furthermore, heterogeneity among studies was assessed by the Cochran’s Q-test and quantified by the I² statistic (22, 23). Thus, I²=100% indicated maximal heterogeneity and I²=0% indicated no heterogeneity (23). The preceding analysis was first applied on all chemokines concentrations, then on each specific type of chemokine. Moreover, subgroup analysis was conducted to explore whether gender (Male and Female subgroups), age (≤60 and >60 subgroups), and method for measuring chemokines (Luminex method and ELISA method subgroups) had any influence on the results. Sensitivity analysis was carried out using the leave-one-out method. When the number of studies reporting the main results was 10 or more, publication bias was assessed using the funnel plot and Egger’s test (24). The significance level was defined as p<0.05 in all analyses, and all tests were two-sided.

Results

Literature Search

The search strategy retrieved a total of 8,775 studies, of which 2,720 were from PubMed, 3,780 from Embase, 1,386 from Web of Science, and 889 from Cochrane Library. Thus, 7,922 articles were excluded for being unrelated to this study. Then the full texts of the 853 articles were assessed, and it was found that 755 articles were not eligible for this study, hence they were excluded. Finally, 98 articles were included in the meta-analysis. Appendix 1 shows a flow diagram presenting the selection process of the included studies.

Characteristics of Eligible Studies

Characteristics of the included studies are shown in Table 1 and Appendix 2. Moreover, Appendix 3 shows a systematic summary of the classification of chemokines and the distribution of the cell type of chemokine receptors such as neutrophils, monocytes, mast cells, basophils, dendric cells, CD8 T cells, and natural killer (NK) cells, in the immune microenvironment. As regards T2DM, the eligible articles included 19 different CC chemokines comparisons involving 7,558 patients and 6,140 controls (Figure 1 and Appendix 4 show forest plots for these chemokines). Also, the eligible articles included a comparison of 13 different CXC chemokines involving 4,145 patients and 4,893 controls (Figure 1 and Appendix 5 show forest plots for these chemokines). For PDM, the eligible articles included 7 different chemokines comparisons involving 3,005 patients and 3,541 controls (Figure 2 and Appendix 6 show forest plots for these chemokines).

Figure 1

Figure 2

Considering sample types of chemokines, 62 eligible studies measured chemokines from peripheral blood samples, 13 from serum samples, 10 from plasma samples, and 13 from other samples. Additionally, the included studies were conducted in China (23), India (9), Italy (6), Japan (6), Germany (5), Iran (5), Turkey (4), USA (4), Brazil (3), Poland (3), South Korea (3), Spain (3), and other regions (24). Also, 69 studies used ELISA to determine chemokines, 10 studies used Luminex, and 19 studies used other methods. The study quality assessment indicated that NOS scores varied between 5 and 8, with 55 studies classified as moderate quality and 44 classified as high quality.

Main Outcomes

Table 2 shows the subgroup analysis of chemokines concentrations between T2DM patients and controls; and between PDM participants and controls. Thus, as regards CC chemokines, CCL1 concentrations were compared between 20 T2DM patients and 50 controls. Therefore, concentrations of CCL1 were significantly higher in patients with T2DM than in controls (SMD=0.69; 95% CI: 0.28 - 1.09; Figure 1). Besides, no significant heterogeneity was observed in the included studies (I²=0). Further, CCL2 concentrations were compared between 3,856 T2DM patients and 2,958 controls, and the results indicated that CCL2 concentrations were in patients with T2DM than in the controls (SMD=1.51; 95% CI: 1.15 - 1.88; Figure 1), but with significant heterogeneity (I²=97%). Also, the CCL4 concentrations were compared between 829 T2DM patients and 411 controls. The results showed higher concentrations of CCL4 in T2DM patients than in the controls (SMD=1.14; 95% CI: 0.51 - 1.78; Figure 1), but with significant heterogeneity (I²=94%). Similarly, the concentrations of CCL5 were compared between 800 T2DM patients and 884 controls, and it was found that these concentrations were higher in T2DM patients than in the controls (SMD=1.42; 95% CI: 0.56 - 2.29; Figure 1), but with significant heterogeneity (I²=98%). In addition, CCL11 concentrations were compared between 621 T2DM patients and 793 controls, and the results showed that T2DM patients had higher concentrations of CCL11 than the controls (SMD=0.77; 95% CI: 0.20 - 1.34; Figure 1), but with significant heterogeneity (I²=95%).

Considering CXC chemokines, concentrations of CXCL8 were compared between 1,405 T2DM patients and 1,599 controls, and the results indicated that T2DM patients had higher concentrations of CXCL8 than the controls (SMD=1.18; 95% CI: 0.64 - 1.72; Figure 1), but with significant heterogeneity (I²=97%). Additionally, CXCL10 concentrations were compared between 1,116 T2DM patients and 1,461 controls, and the results showed that the concentrations of CXCL10 were higher in patients with T2DM than the controls (SMD=0.87; 95% CI: 0.32 - 1.42; Figure 1), but with significant heterogeneity (I²=97%). Also, the concentrations of CX3CL1 were compared between 539 T2DM patients and 510 controls, and the results showed higher concentrations of CX3CL1 in patients with T2DM than in the controls (SMD=1.45; 95% CI: 0.42 - 2.48; Figure 1) but with significant heterogeneity (I²=97%). However, when all chemokines concentrations, taken together, were compared between 3,005 PDM patients and 3,541 controls, no significant difference was observed (SMD=0.20; 95% CI: -0.09 - 0.50; Figure 2). Nevertheless, when specific chemokines concentrations were considered, there were lower concentrations of CCL2 in patients with PDM than in the controls (SMD=-0.11; 95% CI: -0.19 - -0.03; Figure 2); but there was no significant difference in the concentrations of the following chemokines between patients with PDM and the controls: CCL5 (SMD=0.79; 95% CI: -0.04 - 1.61; Figure 2), CXCL8 (SMD=0.09; 95% CI: -0.91 - 1.09; Figure 2), and CXCL10 (SMD=0.10; 95% CI: -0.57 - 0.77; Figure 2). In addition, funnel plots indicated low probability of publication bias (Appendix 7). Moreover, sensitivity analysis indicated that any single study influenced little change in the SMD of chemokines concentrations in PDM and T2DM. Appendix 8 shows in more detail results of the analysis of the chemokines concentration differences between patients with T2DM or PDM and the controls.

Considering subgroups according to gender, the results indicated that concentrations of chemokines in the female T2DM patients (SMD=1.11; 95% CI: 0.79 - 1.43; Table 3) and male T2DM patients (SMD=1.11; 95% CI: 0.87 - 1.35; Table 3) were significantly higher than in the controls. However, no significant difference was observed in the male PDM participants (SMD=-0.02; 95% CI: -0.41 - 0.37; Table 3). Furthermore, subgroup analysis indicated that the concentrations of chemokines were higher in the over 60 years old with T2DM than in their counterparts forming the control group (SMD=1.39; 95% CI: 1.12 - 1.65; Table 3). Similarly, the concentrations of chemokines were higher in the under 60 years old with T2DM than in their counterparts forming the control group (SMD=0.95; 95% CI: 0.69 - 1.22; Table 3) in T2DM, but with a lower effect value (SMD) than in the over 60 years old group. However, no significant difference was observed in the PDM participants as regards to age differences. Considering methods for measuring chemokines (Luminex method and ELISA method subgroups), subgroup analysis indicated that the T2DM patients had higher concentrations of chemokines than the controls, when chemokines were measured using the Luminex method (SMD=0.71; 95% CI: 0.14 - 1.28; Table 3) and the ELISA method (SMD=1.32; 95% CI:1.05 - 1.59; Table 3). However, no significant difference was observed between the PDM participants and the controls when the ELISA method was used (SMD=0.17; 95% CI: -0.20 - 0.55; Table 3).

Discussion

To the best of our knowledge, this is the first systematic review and meta-analysis to explore the association between chemokines concentrations and T2DM/PDM. We identified and systematically reviewed 98 studies. We found that the concentrations of CC chemokines (e.g. CCL1, CCL2, CCL4, CCL5 and CCL11), CXC chemokines (e.g. CXCL8 and CXCL10) and CX3CL1 chemokines were significantly higher in patients with T2DM than in the controls. However, no significant difference in these concentrations was observed between patients with PDM and the controls. These results suggest that some chemokines may play a key role in modulating the pathological process of the progression of PDM to T2DM. Exploring the role of unique chemokines, produced from the immune system, could help to provide a novel therapeutic target for T2DM.

Inflammation appears to be involved in the interplay among chemokines and progression of T2DM, as this has been shown in other several pathological conditions (121–124). Moreover, recent reviews suggested that chemokines, CXCL10, CXCL9, and CXCL11, are implicated in the pathogenesis of autoimmune diseases such as autoimmune thyroiditis, type 1 diabetes, Graves disease, Thyroid eye disease, and Addison’s disease (125–128). Also, evidence indicates CCL2 and CXCL10 chemokine modulations by cytokines and PPARγ agonist in Graves’ ophthalmopathy (129). In this regard, PPARγ agonist activation plays an inhibitory role on CXCL10, but stimulates the release of CCL2. Moreover, a recent systematic review and meta-analysis suggested that chemokines, CCL3, CCL4, CCL5, CCL20, CXCL8 and CXCL11, are implicated in the pathogenesis of non-alcoholic fatty liver disease, post-traumatic stress disorder, and also different types of cancers (130–133).

The chemokines system plays a variety of roles in the T2DM microenvironment. Pancreatic islets and peri-pancreatic adipose tissue (PAT) are exposed to an early damage by genetic or environmental factors and start to secrete numerous pro-inflammatory chemokines. The chemokines and their receptors can also cause a variety of immune cells to enter the pancreatic islets and PAT site to play the role of immune attack. Pro-inflammatory chemokines are bound to their receptors; hence activating the nuclear factor-kappaB (NF-κB/IκBα), and Adenosine 5-monophosphate-activated protein kinase (AMPK) activation pathway, which stimulates a proinflammatory condition. Free fatty acid may also activate inflammatory pathways that may lead to DNA damage.

Moreover, dysfunction of free fatty acid can lead to superoxide and subsequent hydrogen peroxide, which can combine with nitric oxide, such as peroxynitrite (ONOO-), to create peroxynitrite. The compromised mitochondrial electron transport chain (ETC) and the reducing adenosine triphosphate (ATP) synthesis will further lead to the production of reactive oxygen species/reactive nitrogen species (ROS/RNS).

All these processes impact the endoplasmic reticulum (ER) stress, leading to a reduction in the ability to secrete insulin. Moreover, T2DM progression is characterized by progressive secretion of pro-inflammatory chemokines/cytokines caused by β cell damage. Due to this process, various immune cell types (i.e., neutrophils, macrophages, Natural killer (NK) cell, dendritic cell and specifically T cells) are recruited in the pancreatic tissue. These immune cells further release more innate inflammatory cytokines, which contribute to β cell death (Figure 3).

Figure 3

Further, studies have shown that the progression of T2DM is often accompanied by chronic silent inflammation (6, 134). Thus, various pro-inflammatory chemokines mediators are involved in the pathogenesis of T2DM, and this may further alter the normal structure of β-cells by inducing pancreatic islet’s apoptosis (134). Therefore, it may be suggested that the association between T2DM and chemokines may be bidirectional (7).

Referring to higher concentrations of CCL1 in patients with T2DM than in controls, a previous study suggested that CCL1 attracts monocytes, macrophages, Th2 cells and Treg cells by interacting with cell surface chemokine receptor CCR8 (135). Among these monocytes, macrophages release a variety of proinflammatory cytokines (IL-1β and IL-6), which can be released into the systemic circulation (136). Besides, IL-1β can induce inflammation by binding to interleukin-1 receptor type I, which reduces the expression of insulin receptor substrate-1 (IRS-1) at the ERK-dependent transcriptional level and ERK-independent post-transcriptional level, resulting in impaired insulin secretion of islet β cells (137). Moreover, the mechanism of IR induced by IL-6 is complex in that it could not only suppress the lipoprotein lipase that consecutively increase the plasma levels of triglycerides, but could also prevent the metabolism of non-oxidative glucose (138). This is consistent with our result that CCL1 may play a decisive role during immunoregulation in the pathogenesis of T2DM.

Furthermore, in relation to the findings of this study, CCL2, CCL4, CCL5 and CCL11 are considered as pro-inflammatory chemokines. For example, the CCL2 is a chemokine specifically for CCR2 receptors, whereas the CCL4, CCL5 and CCL11 exert a wide range of activities through the CCR5 receptors (139). Among the various chemokine receptors, CCR2, and CCR5 are the most important receptors that play a central role in the pathogenesis of T2DM, and this is consistent with our results. Also, Szalai et al. found that a polymorphism in CCR2 expression has been associated with the progression of IR in children (140). In addition, it has been found that fat cells can secrete CCR2 in an inactive form. Activated CCR2 can induce the expression of various inflammatory genes and reduce insulin-dependent glucose uptake. Further, adipocytes can also secrete CCL2 and CCL3, which is an effective signal for macrophage recruitment (141).

Also, upregulation of CCL2 and CCL3 by adipocytes may contribute to the progression of IR in adipose tissue and peripheral tissues. Although the results showed lower concentrations of CCL2 in PDM than in the controls, the effect value was low. Therefore, future studies need to further explore the dose-response relationship and specific pathophysiological role of CCL2 in the pathogenesis, severity and progression of PDM. In addition, Chang et al. found that CCL4 and CCR5 might provide potential therapeutic targets in T2DM (142). With the dysfunction of β cells in T2DM, even before β cells are widely damaged, the CCL4 concentrations may rise ahead of time. Also, while there are β cells death and early islet graft loss, inflammatory stimuli with a CD40-CD40L interaction could induce the secretion of CCL4 through the Raf/MEK/ERK and NF-κB pathways in pancreatic islets (143). Therefore, CCL4 concentrations may be caused by the initial inflammatory damage of islet β cells.

As a member of the CXC chemokine family, the function of CXCL8 is to induce chemotaxis in its target cells, like T-cells, neutrophil granulocytes, basophils and adipocytes. There are many receptors capable of binding to CXCL8, but the most affinity to CXCL8 are receptors CXCR1 and CXCR2 (144). Clinical studies have shown that CXCL8 secreted by adipocytes may be related to complications, such as T2DM, which are related to the excess accumulation of intra-abdominal fat (145). Increasing evidence suggests that the intra-abdominal fat accumulation is closely related to decreased insulin sensitivity and increased T2DM pathophysiology (145, 146). These findings concur with the result of this study that T2DM patients have higher concentrations of CXCL8. Thus, CXCL8 may mediate the down regulation of adiponectin in obesity. Adiponectin can prevent the impairment of insulin signaling; hence CXCL8 may play a crucial and causal role in obesity-linked IR and T2DM (6).

Furthermore, as a member of the CXC chemokine family, CXCL10 is secreted by various cell types, such as monocytes, endothelial cells and fibroblasts. In this study, T2DM patients have been shown to have higher concentrations of CXCL10. This is possible because CXCL10 functions via chemokine receptor CXCR3 and toll-likereceptor 4 (TLR4) (97, 147). In addition, CXCL10 has been attributed to several roles, such as activating T-lymphocytes and monocytes/macrophages. Also, clinical studies have shown that the concentrations of CXCL10 secreted by patients with T2DM are higher than in the control group (97). Therefore, there is a hypothesis that the interferon gamma-inducible CXCL10 plays an important role in triggering β cell destruction. Also, CXCL10 can impair insulin secretion and decrease β cell viability. The specific mechanism may be that the CXCL10 induced β cells can sustain the activation of protein kinase B (Akt), c-Jun N-terminal kinase (JNK), and cleavage of p21-activated protein kinase 2 (PAK-2); hence switching Akt signals from proliferation to apoptosis. These effects were mediated by the TLR4 (147, 148).

As the only known member of the CX3C chemokine family, CX3CL1 has the chemoattractive activity for T cells, NK cells, and monocytes (149). Clinical studies have shown that plasma CX3CL1 is significantly reduced in the control group compared to patients with T2DM, which is consistent with our results (34). The CX3CL1 is known to mediate leukocyte chemotaxis, adhesion and survival, which causes chronic adipose inflammation, and is closely associated with T2DM. Specifically, CX3CL1 may play a key role in recruiting monocytes to adipose tissue, and then leads to systemic inflammation and IR (150). The influence of CX3CL1-mediated mechanism of leukocyte chemotaxis and adhesion might be a way that leads to T2DM and adipocyte dysfunction. Additionally, studies have shown that high CX3CL1 levels can lead to depletion of functional reserves of β cells and chronic hyperinsulinemia, while inhibition of CX3CL1/CX3CR1 system can regulate pancreatic islet β-cell function (149). For example, Rosiglitazone inhibits CX3CL1 expression and CX3CL1/CX3CR1 signaling by activating peroxisome proliferator-activated receptor γ (PPAR-γ), which can achieve the effect of treating IR and T2DM (151).

The subgroup results according to gender indicated that concentrations of chemokines were significantly higher in the T2DM female and male patients than in their respective control groups. However, no significant difference in these concentrations was observed between male PDM participants and controls. This result may suggest that gender differences are related to PDM. This is consistent with the results of some previous studies which suggested that gender hormones may play an important role in the pathogenesis of impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) (152, 153). In general, IFG is more prevalent in males, whereas IGT is more prevalent in females (152, 154). Also, some studies on preclinical gender-related genetic differences have shown that chemokine gene polymorphisms are associated with gender (155, 156). Therefore, we conjecture that the high expression of chemokines in females in the early stage of diabetes may have an indirect effect on insulin resistance through pro-inflammatory effect, and may also play a direct role in impaired glucose tolerance affect by regulating metabolism, inflammation and insulin signaling pathways. Thus, gender-specific differences in the pathogenesis of PDM suggest consideration for personalized treatment of PDM according to gender.

Furthermore, subgroup analysis showed that, although the concentrations of chemokines were higher in both age groups with T2DM, the over 60 years old had a higher effect value (SMD) than the under 60 years. In fact, previous studies have shown that chemokines are deeply influenced by aging and age-related inflammation kinetics (157, 158). Moreover, these results suggest that the elderly cannot control excessive inflammatory state causing the pro-inflammatory cytokines (158–160). Therefore, we speculate that the high concentrations of chemokines with excessive inflammatory response in the elderly T2DM patients could be the response to the perturbations in their imbalanced immune microenvironment. How to improve this imbalanced immune microenvironment may be a novel issue that would indicate that therapies should target the immune microenvironment in the treatment of T2DM in the elderly (161).

In addition, subgroup analysis indicated that the T2DM patients had higher concentrations of chemokines than the controls, when chemokines were measured using the Luminex method and the ELISA method. However, no significant difference was observed between the PDM participants and the controls when the ELISA method was used. These results may suggest that there was a potential heterogeneity when chemokines detection method was ELISA, and Luminex method has higher sensitivity and specificity. Alternatively, in consisted with our results, some previous studies showed that the Luminex method is permit the rapid, simultaneous detection of multiple inflammatory factors and chemokines with high sensitivity and specificity (162, 163). Therefore, we recommend the use of Luminex method for chemokines detection in future studies.

This systematic review and meta-analysis has some strengths and limitations. For example, this study analyzed various chemokines using broad inclusion criteria. Therefore, the use of a comprehensive search strategy, with multiple variant names of chemokines when searching in the databases, is the main strength of this study. Besides, this study has helped to clarify the previously reported results on the association between chemokines and T2DM or PDM. Therefore, the findings might open up new perspectives in T2DM for early diagnosis, identification of novel biomarkers, and novel targets for pharmacological interventions.

However, the number of available studies on chemokines (e.g., CCL13, CCL15, CCL21, CXCL2 and CXCL6) is limited, which may lead to biased or imprecise results. Accordingly, future studies should pay more attention to the investigation of the role of chemokines in T2DM and PDM. Also, there was lack of the following information in the included original studies: BMI, blood pressure, smoking and drinking. Therefore, it was difficult to quantify various confounding factors at individual level. In this regard, confounding factors should be better reported in future studies. Further, the original data for this study came from cross-sectional or case-control studies, which may not make causal inference. Therefore, population-based cohort studies are needed in the future to establish causal relationships between chemokines and T2DM or PDM.

Conclusions

This systematic review and meta-analysis suggests that the progression of PDM to T2DM may be associated with elevated concentrations of chemokines (CCL1, CCL2, CCL4, CCL5, CCL11, CXCL8, CXCL10 and CX3CL1). Understanding the complex interplay of chemokines and the progression of PDM to T2DM may facilitate the identification of potential targets in prevention, diagnosis, and treatment of T2DM. The value of these chemokines in clinical practice should be elucidated in further studies.

Author Contributions

XP and AL contributed to the study design, while SW and AK contributed to the data collection. Statistical analyses and interpretation of results were performed by XP and AK, whereas XP and SW drafted the manuscript and edited the language. All authors contributed to the article and approved the submitted version.

Funding

The research is financially supported by Hunan Provincial Key Laboratory of Clinical Epidemiology and the Hunan Provincial Key Research and Development Program (2018SK2065), China.

References

• 1

  N. C. D. R. F. Collaboration. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet (London England) (2016) 387(10027):1513–30. doi: 10.1016/S0140-6736(16)00618-8

  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  ChatterjeeSKhuntiKDaviesMJ. Type 2 diabetes. Lancet (2017) 389(10085):2239–51. doi: 10.1016/s0140-6736(17)30058-2

  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  EsserNPaquotNScheenA. J.Anti-inflammatory agents to treat or prevent type 2 diabetes, metabolic syndrome and cardiovascular disease. Expert Opin Investig Drugs (2015) 24(3):283–307. doi: 10.1517/13543784.2015.974804

  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  LiuCFengXLiQWangYLiQHuaM. Adiponectin, TNF-alpha and inflammatory cytokines and risk of type 2 diabetes: A systematic review and meta-analysis. Cytokine (2016) 86:100–9. doi: 10.1016/j.cyto.2016.06.028

  • CrossRef
  • Google Scholar
  • View reference in article

• 5

  RehmanKAkashMS. Mechanisms of inflammatory responses and development of insulin resistance: how are they interlinked? J BioMed Sci (2016) 23(1):87. doi: 10.1186/s12929-016-0303-y

  • CrossRef
  • Google Scholar
  • View reference in article

• 6

  TretjakovsPJurkaABormaneIMackevicsVMikelsoneIBalodeLet al. Relation of inflammatory chemokines to insulin resistance and hypoadiponectinemia in coronary artery disease patients. Eur J Intern Med (2009) 20(7):712–7. doi: 10.1016/j.ejim.2009.08.004

  • CrossRef
  • Google Scholar
  • View reference in article

• 7

  Hosseini-ZijoudSM. Chemokines: Small molecules participate in diabetes. Iran J Allergy Asthma Immunol (2013) 12(1):S65.

  • Google Scholar
  • View reference in article

• 8

  LiJJZhuHShiCH. Chemokines and diabetic retinopathy. Int J Ophthalmol (2011) 11(1):91–3. doi: 10.3969/j.issn.1672-5123.2011.01.030

  • CrossRef
  • Google Scholar
  • View reference in article

• 9

  OchoaOTorresFMShiremanPK. Chemokines and diabetic wound healing. Vascular (2007) 15(6):350–5. doi: 10.2310/6670.2007.00056

  • CrossRef
  • Google Scholar
  • View reference in article

• 10

  TabákAGHerderCRathmannWBrunnerEJKivimäkiM. Prediabetes: a high-risk state for diabetes development. Lancet (2012) 379(9833):2279–90. doi: 10.1016/s0140-6736(12)60283-9

  • CrossRef
  • Google Scholar
  • View reference in article

• 11

  Wender-OzegowskaEMichałowska-WenderGZawiejskaAPietrygaMBrazertJWenderM. Concentration of chemokines in peripheral blood in first trimester of diabetic pregnancy. Acta Obstetr Gynecol Scand (2018) 87(1):14–9. doi: 10.1080/00016340701698724

  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  SindhuSAkhterNArefanianHAl-RoubAAAliSWilsonAet al. Increased circulatory levels of fractalkine (CX3CL1) are associated with inflammatory chemokines and cytokines in individuals with type-2 diabetes. J Diabetes Metab Disord (2017) 16(1). doi: 10.1186/s40200-017-0297-3

  • CrossRef
  • Google Scholar
  • View reference in article

• 13

  MoherDLiberatiATetzlaffJAltmanDG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Bmj (2009) 339:b2535. doi: 10.1136/bmj.b2535

  • CrossRef
  • Google Scholar
  • View reference in article

• 14

  PanXKamingaACWenSWWuXAcheampongKLiuA. Dopamine and Dopamine Receptors in Alzheimer’s Disease: A Systematic Review and Network Meta-Analysis. Front Aging Neurosci (2019) 11:175. doi: 10.3389/fnagi.2019.00175

  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  PanXWuXKamingaACWenSWLiuA. Dehydroepiandrosterone and Dehydroepiandrosterone Sulfate in Alzheimer’s Disease: A Systematic Review and Meta-Analysis. Front Aging Neurosci (2019) 1:61. doi: 10.3389/fnagi.2019.00061

  • CrossRef
  • Google Scholar
  • View reference in article

• 16

  PanXKamingaACWenSWLiuA. Catecholamines in Post-traumatic Stress Disorder: A Systematic Review and Meta-Analysis. Front Mol Neurosci (2018) 1:450. doi: 10.3389/fnmol.2018.00450

  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  PanXWangZWuXWenSWLiuA. Salivary cortisol in post-traumatic stress disorder: a systematic review and meta-analysis. BMC Psychiatry (2018) 18(1):324. doi: 10.1186/s12888-018-1910-9

  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  PanXKamingaACWenSWAcheampongKLiuA. Omentin-1 in diabetes mellitus: A systematic review and meta-analysis. PloS One (2019) 14(12):e0226292. doi: 10.1371/journal.pone.0226292

  • CrossRef
  • Google Scholar
  • View reference in article

• 19

  PanXWenSWBestmanPLKamingaACAcheampongKLiuA. Fetuin-A in Metabolic syndrome: A systematic review and meta-analysis. PloS One (2020) 15(3):e0229776. doi: 10.1371/journal.pone.0229776

  • CrossRef
  • Google Scholar
  • View reference in article

• 20

  PanXKamingaACChenJLuoMLuoJ. Fetuin-A and Fetuin-B in Non-Alcoholic Fatty Liver Disease: A Meta-Analysis and Meta-Regression. Int J Environ Res Public Health (2020) 17(8). doi: 10.3390/ijerph17082735

  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  HigginsJPThompsonSGDeeksJJAltmanDG. Measuring inconsistency in meta-analyses. Bmj (2003) 327(7414):557–60. doi: 10.1136/bmj.327.7414.557

  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  PanXZhaoLLuoJLiYZhangLWuTet al. Access to bike lanes and childhood obesity: A systematic review and meta-analysis. Obes Rev (2020). doi: 10.1111/obr.13042

  • CrossRef
  • Google Scholar
  • View reference in article

• 23

  HigginsJPThompsonSG. Quantifying heterogeneity in a meta-analysis. Stat Med (2002) 21(11):1539–58. doi: 10.1002/sim.1186

  • CrossRef
  • Google Scholar
  • View reference in article

• 24

  EggerMDavey SmithGSchneiderMMinderC. Bias in meta-analysis detected by a simple, graphical test. Bmj (1997) 315(7109):629–34. doi: 10.1136/bmj.315.7109.629

  • CrossRef
  • Google Scholar
  • View reference in article

• 25

  AdelaRReddyPNCGhoshTSAggarwalSYadavAKDasBet al. Serum protein signature of coronary artery disease in type 2 diabetes mellitus. J Transl Med (2019) 7(1):17. doi: 10.1186/s12967-018-1755-5

  • CrossRef
  • Google Scholar
  • View reference in article

• 26

  AfaridehMGhanbariPNoshadSGhajarANakhjavaniMEsteghamatiA. Raised serum 25-hydroxyvitamin D levels in patients with active diabetic foot ulcers. Br J Nutr (2016) 115(11):1938–46. doi: 10.1017/S0007114516001094

  • CrossRef
  • Google Scholar
  • View reference in article

• 27

  AhmedSFShabayekMIGhanyMEAEl-HefnawyMHEl-MesallamyHO. Role of CTRP3, CTRP9 and MCP-1 for the evaluation of T2DM associated coronary artery disease in Egyptian postmenopausal females. PloS One (2018) 13(12). doi: 10.1371/journal.pone.0208038

  • CrossRef
  • Google Scholar
  • View reference in article

• 28

  AlickaMMajorPWysockiMMaryczK. Adipose-Derived Mesenchymal Stem Cells Isolated from Patients with Type 2 Diabetes Show Reduced “Stemness” through an Altered Secretome Profile, Impaired Anti-Oxidative Protection, and Mitochondrial Dynamics Deterioration. J Clin Med (2019) 8(6). doi: 10.3390/jcm8060765

  • CrossRef
  • Google Scholar
  • View reference in article

• 29

  Almeida-PitittoBRibeiro-FilhoFFLotufoPABensenorIMFerreiraSR. Novel biomarkers of cardiometabolic risk are associated with plasma glucose within non-diabetic range. The Brazilian Longitudinal Study of Adult Health - ELSA-Brasil. Diabetes Res Clin Pract (2015) 109(1):110–6. doi: 10.1016/j.diabres.2015.04.021

  • CrossRef
  • Google Scholar
  • View reference in article

• 30

  Alvarado-VazquezPAGrosickRLMoracho-VilrrialesCWardEThreattTRomero-SandovalEA. Cytokine production capabilities of human primary monocyte-derived macrophages from patients with diabetes mellitus type 2 with and without diabetic peripheral neuropathy. J Pain Res (2019) 12:69–81. doi: 10.2147/JPR.S186372

  • CrossRef
  • Google Scholar
  • View reference in article

• 31

  AravindhanVKevinkumarVDhamodharanUViswanathanV. Serum levels of chemokines IP-10, IL-8 and SDF-1 serve as good biomarkers for diabetes-tuberculosis nexus. J Diabetes Its Complicat (2018) 32(9):857–62. doi: 10.1016/j.jdiacomp.2018.07.001

  • CrossRef
  • Google Scholar

• 32

  BalaMKoppAWurmSBüchlerCSchölmerichJSchäfflerA. Type 2 diabetes and lipoprotein metabolism affect LPS-induced cytokine and chemokine release in primary human monocytes. Exp Clin Endocrinol Diabetes119(6):370–6. doi: 10.1055/s-0030-1268413

  • CrossRef
  • Google Scholar

• 33

  BaldaneSKendirICKiracCOIpekciSTekinGUnluAet al. Effects Of Glucose Ingestion On Serum Fractalkine Levels In Healthy Subjects And Newly Diagnosed Type 2 Diabetic Patients. J Med Biochem37(3):373–8. doi: 10.1515/jomb-2017-0070

  • CrossRef
  • Google Scholar

• 34

  BaldaneSIpekciSHEkinAAbusogluSUnluAKebapcilarL. Evaluation of fractalkine (FKN) and secreted frizzled-related protein 4 (SFRP-4) serum levels in patients with prediabetes and type 2 diabetes. Bratislava Med J-Bratislavske Lekarske Listy (2018) 119(2):112–5. doi: 10.4149/bll_2018_021

  • CrossRef
  • Google Scholar

• 35

  BarchettaICiminiFACapocciaDDe GioannisRPorziaAMainieroFet al. WISP1 Is a Marker of Systemic and Adipose Tissue Inflammation in Dysmetabolic Subjects With or Without Type 2 Diabetes. J Endocr Soc (2017) 1(6):660–70. doi: 10.1210/js.2017-00108

  • CrossRef
  • Google Scholar

• 36

  Cataño CañizalesYGUresti RiveraEEGarcía JacoboREPortales PerezDPYadiraBRodriguez RiveraJGet al. Increased Levels of AIM2 and Circulating Mitochondrial DNA in Type 2 Diabetes. Iran J Immunol (2018) 15(2):142–55.

  • Google Scholar

• 37

  CaponeFGuerrieroEColonnaGMaioPMangiaAMarfellaRet al. The Cytokinome Profile in Patients with Hepatocellular Carcinoma and Type 2 Diabetes. PloS One (2015) 10(7):e0134594. doi: 10.1371/journal.pone.0134594

  • CrossRef
  • Google Scholar

• 38

  ChaJJHyunYYJeeYHLeeMJHanKHKangYSet al. Plasma leptin concentrations are greater in type II diabetic patients and stimulate monocyte chemotactic peptide-1 synthesis via the mitogen-activated protein kinase/extracellular signal-regulated kinase pathway. Kidney Res Clin Pract (2012) 31(3):177–85. doi: 10.1016/j.krcp.2012.06.004

  • CrossRef
  • Google Scholar

• 39

  ChangC-CWuC-LSuW-WShihK-LTarngD-CChouC-Tet al. Interferon gamma-induced protein 10 is associated with insulin resistance and incident diabetes in patients with nonalcoholic fatty liver disease. Sci Rep (2015) 5. doi: 10.1038/srep10096

  • CrossRef
  • Google Scholar

• 40

  ChaoPCHuangCNHsuCCYinMCGuoYR. Association of dietary AGEs with circulating AGEs, glycated LDL, IL-1α and MCP-1 levels in type 2 diabetic patients. Eur J Nutr (2017) 49(7):429–34. doi: 10.1007/s00394-010-0101-3

  • CrossRef
  • Google Scholar

• 41

  ChenHZhangXLiaoNWenF. Assessment of biomarkers using multiplex assays in aqueous humor of patients with diabetic retinopathy. BMC Ophthalmol (2017) 17(1):176. doi: 10.1186/s12886-017-0572-6

  • CrossRef
  • Google Scholar

• 42

  ChengMLiBYLiXLWangQZhangJHJingXJet al. Correlation between serum lactadherin and pulse wave velocity and cardiovascular risk factors in elderly patients with type 2 diabetes mellitus. Diabetes Res Clin Pract (2012) 95(1):125–31. doi: 10.1016/j.diabres.2011.09.030

  • CrossRef
  • Google Scholar

• 43

  CheungCMGVaniaMAngMCheeSPLiJ. Comparison of aqueous humor cytokine and chemokine levels in diabetic patients with and without retinopathy. Mol Vision (2012) 18(87-88):830–7.

  • Google Scholar

• 44

  CiminiFABarchettaIPorziaAMainieroFCostantinoCBertocciniLet al. Circulating IL-8 levels are increased in patients with type 2 diabetes and associated with worse inflammatory and cardiometabolic profile. Acta Diabetol (2017) 54(10):961–7. doi: 10.1007/s00592-017-1039-1

  • CrossRef
  • Google Scholar

• 45

  CiminiFAD’EliseoDBarchettaIBertocciniLVelottiFCavalloMG. Increased circulating granzyme B in type 2 diabetes patients with low-grade systemic inflammation. Cytokine (2019) 115:104–8. doi: 10.1016/j.cyto.2018.11.019

  • CrossRef
  • Google Scholar

• 46

  DanielssonPTruedssonLErikssonKFNorgrenL. Inflammatory markers and IL-6 polymorphism in peripheral arterial disease with and without diabetes mellitus. Vasc Med (2005) 10(3):191–8. doi: 10.1191/1358863x05vm617oa

  • CrossRef
  • Google Scholar

• 47

  DavìGTuttolomondoASantilliFBasiliSFerranteEDi RaimondoDet al. CD40 ligand and MCP-1 as predictors of cardiovascular events in diabetic patients with stroke. J Atheroscl Thromb (2009) 16(6):707–13. doi: 10.5551/jat.1537

  • CrossRef
  • Google Scholar

• 48

  DesfaitsACSerriORenierG. Normalization of plasma lipid peroxides, monocyte adhesion, and tumor necrosis factor-alpha production in NIDDM patients after gliclazide treatment. Diabetes Care (1998) 21(4):487–93. doi: 10.2337/diacare.21.4.487

  • CrossRef
  • Google Scholar

• 49

  DegirmenciIOzbayerCKebapciMNKurtHColakEGunesHV. Common variants of genes encoding TLR4 and TLR4 pathway members TIRAP and IRAK1 are effective on MCP1, IL6, IL1beta, and TNFalpha levels in type 2 diabetes and insulin resistance. Inflammation Res (2019) 68(9):801–14. doi: 10.1007/s00011-019-01263-7

  • CrossRef
  • Google Scholar

• 50

  DerakhshanRArababadiMKAhmadiZKarimabadMNSalehabadiVAAbedinzadehMet al. Increased circulating levels of SDF-1 (CXCL12) in type 2 diabetic patients are correlated to disease state but are unrelated to polymorphism of the SDF-1β gene in the Iranian population. Inflammation (2012) 35(3):900–4. doi: 10.1007/s10753-011-9391-8

  • CrossRef
  • Google Scholar

• 51

  El-MesallamyHOHamdyNMSalmanTMIbrahimSM. Adiponectin and sE-selectin concentrations in relation to inflammation in obese type 2 diabetic patients with coronary heart disease. Angiology (2012) 63(2):96–102. doi: 10.1177/0003319711408587

  • CrossRef
  • Google Scholar

• 52

  FengSYuHYuYGengYLiDYangCet al. Levels of inflammatory cytokines IL-1β, IL-6, IL-8, IL-17A, and TNF-α in aqueous humour of patients with diabetic retinopathy. J Diabetes Res (2018) (2018). doi: 10.1155/2018/8546423

  • CrossRef
  • Google Scholar

• 53

  FunatsuHNomaHMimuraTEguchiSHoriS. Association of vitreous inflammatory factors with diabetic macular edema. Ophthalmology (2009) 116(1):73–9. doi: 10.1016/j.ophtha.2008.09.037

  • CrossRef
  • Google Scholar

• 54

  GeerlingsSEBrouwerECVan KesselKCGaastraWStolkRPHoepelmanAI. Cytokine secretion is impaired in women with diabetes mellitus. Eur J Clin Invest (2000) 30(11):995–1001. doi: 10.1046/j.1365-2362.2000.00745.x

  • CrossRef
  • Google Scholar

• 55

  GiuliettiAvan EttenEOverberghLStoffelsKBouillonRMathieuC. Monocytes from type 2 diabetic patients have a pro-inflammatory profile. 1,25-Dihydroxyvitamin D(3) works as anti-inflammatory. Diabetes Res Clin Pract (2007) 77(1):47–57. doi: 10.1016/j.diabres.2006.10.007

  • CrossRef
  • Google Scholar

• 56

  GokulakrishnanKAmuthaARanjaniHBibinSYBalakumarMPandeyGKet al. Relationship of Adipokines and Proinflammatory Cytokines among Asian Indians with Obesity and Youth Onset Type 2 Diabetes. Endocr Pract (2015) 21(10):1143–51. doi: 10.4158/EP15741.OR

  • CrossRef
  • Google Scholar

• 57

  GomezJMVilaRCatalinaPSolerJBadimonLSahunM. The markers of inflammation and endothelial dysfunction in correlation with glycated haemoglobin are present in type 2 diabetes mellitus patients but not in their relatives. Glycoconj J (2008) 25(6):573–9. doi: 10.1007/s10719-008-9118-8

  • CrossRef
  • Google Scholar

• 58

  GongFWuJZhouPZhangMLiuJLiuYet al. Interleukin-22 Might Act as a Double-Edged Sword in Type 2 Diabetes and Coronary Artery Disease. Mediators Inflammation (2016) 2016:8254797. doi: 10.1155/2016/8254797

  • CrossRef
  • Google Scholar

• 59

  HamidSGulAHamidQ. Relationship of cytokines and AGE products in diabetic and non-diabetic patients with cataract. Int J Health Sci (Qassim) (2016) 10(4):507–15. doi: 10.12816/0048891

  • CrossRef
  • Google Scholar

• 60

  Hara CdeCFrancaELFagundesDLde QueirozAARudgeMVHonorio-FrancaACet al. Characterization of Natural Killer Cells and Cytokines in Maternal Placenta and Fetus of Diabetic Mothers. J Immunol Res (2016) 2016:7154524. doi: 10.1155/2016/7154524

  • CrossRef
  • Google Scholar

• 61

  HeLWongCKCheungKKYauHCFuAZhaoHLet al. Anti-inflammatory effects of exendin-4, a glucagon-like peptide-1 analog, on human peripheral lymphocytes in patients with type 2 diabetes. J Diabetes Investig (2013) 4(4):382–92. doi: 10.1111/jdi.12063

  • CrossRef
  • Google Scholar

• 62

  HerderCMüller-ScholzeSRatingPKoenigWThorandBHaastertBet al. Systemic monocyte chemoattractant protein-1 concentrations are independent of type 2 diabetes or parameters of obesity: results from the Cooperative Health Research in the Region of Augsburg Survey S4 (KORA S4). Eur J Endocrinol (2006) 154(2):311–7. doi: 10.1530/eje.1.02090

  • CrossRef
  • Google Scholar

• 63

  HerderCIlligTBaumertJMüllerMKloppNKhuseyinovaNet al. RANTES/CCL5 gene polymorphisms, serum concentrations, and incident type diabetes: Results from the MONICA/KORA Augsburg case-cohort study, 1984-2002. Eur J Endocrinol158(5):R1–5. doi: 10.1530/EJE-07-0686

  • CrossRef
  • Google Scholar

• 64

  HernándezCSeguraRMFonollosaACarrascoEFranciscoGSimóR. Interleukin-8, monocyte chemoattractant protein-1 and IL-10 in the vitreous fluid of patients with proliferative diabetic retinopathy. Diabetes Med (2005) 22(6):719–22. doi: 10.1111/j.1464-5491.2005.01538.x

  • CrossRef
  • Google Scholar

• 65

  HirschFFParejaJCGelonezeSRChaimECazzoEGelonezeB. Comparison of metabolic effects of surgical-induced massive weight loss in patients with long-term remission versus non-remission of type 2 diabetes. Obes Surg (2012) 22(6):910–7. doi: 10.1007/s11695-012-0589-0

  • CrossRef
  • Google Scholar

• 66

  HuYYYeSDZhaoLLZhengMWuFZChenY. Hydrochloride pioglitazone decreases urinary cytokines excretion in type 2 diabetes. Clin Endocrinol (Oxf) (2010) 73(6):739–43. doi: 10.1111/j.1365-2265.2010.03878.x

  • CrossRef
  • Google Scholar

• 67

  HuangGMoXLiMXiangYLiXLuoSet al. Autoantibodies to CCL3 are of low sensitivity and specificity for the diagnosis of type 1 diabetes. Acta Diabetol (2012) 49(5):395–9. doi: 10.1007/s00592-012-0380-7

  • CrossRef
  • Google Scholar

• 68

  InayatHAzimMKBalochAA. Analysis of Inflammatory Gene Expression Profile of Peripheral Blood Leukocytes in Type 2 Diabetes. Immunol Invest (2019) 48(6):618–31. doi: 10.1080/08820139.2019.1586917

  • CrossRef
  • Google Scholar

• 69

  KalninovaJJakusVGlejtkovaMKurackaLSandorovaE. Impact of glycemic control on advanced glycation and inflammation in overweight and obese patients with type 2 diabetes mellitus. Bratisl Lek Listy (2014) 115(8):457–68. doi: 10.4149/bll_2014_089

  • CrossRef
  • Google Scholar

• 70

  KangYSSongHKLeeMHKoGJChaDR. Plasma concentration of visfatin is a new surrogate marker of systemic inflammation in type 2 diabetic patients. Diabetes Res Clin Pract (2010) 89(2):141–9. doi: 10.1016/j.diabres.2010.03.020

  • CrossRef
  • Google Scholar

• 71

  KouHDengJGaoDSongAHanZWeiJet al. Relationship among adiponectin, insulin resistance and atherosclerosis in non-diabetic hypertensive patients and healthy adults. Clin Exp Hypertens (2018) 40(7):656–63. doi: 10.1080/10641963.2018.1425414

  • CrossRef
  • Google Scholar

• 72

  Pavan KumarPRadhikaGRaoGVPradeepRSubramanyamCTalukdarRet al. Interferon γ and glycemic status in diabetes associated with chronic pancreatitis. Pancreatology (2012) 12(1):65–70. doi: 10.1016/j.pan.2011.12.005

  • CrossRef
  • Google Scholar

• 73

  KumarNPSridharRBanurekhaVVJawaharMSFayMPNutmanTBet al. Type 2 diabetes mellitus coincident with pulmonary tuberculosis is associated with heightened systemic type 1, type 17, and other proinflammatory cytokines. Ann Am Thorac Soc (2013) 10(5):441–9. doi: 10.1513/AnnalsATS.201305-112OC

  • CrossRef
  • Google Scholar

• 74

  Landers-RamosRQBlumenthalJBPriorSJ. Serum IL-6 and sIL-6R in type 2 diabetes contribute to impaired capillary-like network formation. J Appl Physiol (1985) (2019) 127(2):385–92. doi: 10.1152/japplphysiol.00002.2019

  • CrossRef
  • Google Scholar

• 75

  LareyreFMoratalCChikandeJJean-BaptisteEHassen-KhodjaRNeelsJet al. Investigation of Plasma Inflammatory Profile in Diabetic Patients With Abdominal Aortic Aneurysm: A Pilot Study. Vasc Endovasc Surg (2018) 52(8):597–601. doi: 10.1177/1538574418784717

  • CrossRef
  • Google Scholar

• 76

  LiCKangDSunXLiuYWangJGaoP. The Effect of C-X-C Motif Chemokine 13 on Hepatocellular Carcinoma Associates with Wnt Signaling. BioMed Res Int (2015) 2015:345413. doi: 10.1155/2015/345413

  • CrossRef
  • Google Scholar

• 77

  LiuSHuangJYuFWangJZhangQQuSet al. The expression of chemerin and chemerin receptor in MCD induced NAFLD rat. Diabetes (2011) 60:A686. doi: 10.2337/db11-2585-2680

  • CrossRef
  • Google Scholar

• 78

  LiuTXueRDongLWuHZhangDShenX. Rapid determination of serological cytokine biomarkers for hepatitis B virus-related hepatocellular carcinoma using antibody microarrays. Acta Biochim Biophys Sin (Shanghai) (2011) 43(1):45–51. doi: 10.1093/abbs/gmq111

  • CrossRef
  • Google Scholar

• 79

  LiuniFMRugieroCFeolaMRaoCPistilloPTerraccianoCet al. Impaired healing of fragility fractures in type 2 diabetes: clinical and radiographic assessments and serum cytokine levels. Aging Clin Exp Res (2015) 27(Suppl 1):S37–44. doi: 10.1007/s40520-015-0422-4

  • CrossRef
  • Google Scholar

• 80

  LuPJiXWanJXuH. Activity of Group 2 Innate Lymphoid Cells is Associated with Chronic Inflammation and Dysregulated Metabolic Homoeostasis in Type 2 Diabetic Nephropathy. Scand J Immunol (2018) 87(2):99–107. doi: 10.1111/sji.12637

  • CrossRef
  • Google Scholar

• 81

  MaegdefesselLSchlittAPippigSSchwaabBFingscheidtKRaazUet al. Patients with insulin-dependent diabetes or coronary heart disease following rehabilitation express serum fractalkine levels similar to those in healthy control subjects. Vasc Health Risk Manage (2009) 5:849–57. doi: 10.2147/VHRM.S6829

  • CrossRef
  • Google Scholar

• 82

  MaierRWegerMHaller-SchoberEMEl-ShabrawiYWedrichATheislAet al. Multiplex bead analysis of vitreous and serum concentrations of inflammatory and proangiogenic factors in diabetic patients. Mol Vis (2008) 14:637–43.

  • Google Scholar

• 83

  MangialardiGFerland-McColloughDMaselliDSantopaoloMCordaroASpinettiGet al. Bone marrow pericyte dysfunction in individuals with type 2 diabetes. Diabetologia (2019) 62(7):1275–90. doi: 10.1007/s00125-019-4865-6

  • CrossRef
  • Google Scholar

• 84

  McCarthyCPShresthaSIbrahimNvan KimmenadeRRJGagginHKMukaiRet al. Performance of a clinical/proteomic panel to predict obstructive peripheral artery disease in patients with and without diabetes mellitus. Open Heart (2019) 6(1):e000955. doi: 10.1136/openhrt-2018-000955

  • CrossRef
  • Google Scholar

• 85

  MesiaRGholamiFHuangHClare-SalzlerMAukhilIWalletSMet al. Systemic inflammatory responses in patients with type 2 diabetes with chronic periodontitis. BMJ Open Diabetes Res Care (2016) 4(1):e000260. doi: 10.1136/bmjdrc-2016-000260

  • CrossRef
  • Google Scholar

• 86

  MineSOkadaYTanikawaTKawaharaCTabataTTanakaY. Increased expression levels of monocyte CCR2 and monocyte chemoattractant protein-1 in patients with diabetes mellitus. Biochem Biophys Res Commun (2006) 344(3):780–5. doi: 10.1016/j.bbrc.2006.03.197

  • CrossRef
  • Google Scholar

• 87

  MohamedHGIdrisSBAhmedMFAstromANMustafaKIbrahimSOet al. Influence of type 2 diabetes on local production of inflammatory molecules in adults with and without chronic periodontitis: a cross-sectional study. BMC Oral Health (2015) 15:86. doi: 10.1186/s12903-015-0073-z

  • CrossRef
  • Google Scholar

• 88

  MuraseHSuzukiETajimaYHayashiKNakamuraTNoritakeNet al. Associations of plasma von Willebrand factor ristocetin cofactor activity and 5-hydroxyindole acetic acid concentrations with blood flow in lower-leg arteries in Japanese type 2 diabetic patients with normal ankle-brachial index. J Diabetes Complicat (2012) 26(2):113–7. doi: 10.1016/j.jdiacomp.2012.02.003

  • CrossRef
  • Google Scholar

• 89

  NomuraSShouzuAOmotoSNishikawaMIwasakaT. Benidipine improves oxidized LDL-dependent monocyte and endothelial dysfunction in hypertensive patients with type 2 diabetes mellitus. J Hum Hypertens (2005) 19(7):551–7. doi: 10.1038/sj.jhh.1001863

  • CrossRef
  • Google Scholar

• 90

  OmotoSTaniuraTNishizawaTTamakiTShouzuANomuraS. Anti-atherosclerotic effects of sitagliptin in patients with type 2 diabetes mellitus. Diabetes Metab Syndr Obes (2015) 8:339–45. doi: 10.2147/DMSO.S84545

  • CrossRef
  • Google Scholar

• 91

  PapatheodorouKPapanasNPapazoglouDGiokaTAntonoglouCGlarosDet al. Monocyte chemoattractant protein 1 is correlated with glycemic control and peripheral arterial disease in type 2 diabetic patients with metabolic syndrome. Angiology (2013) 64(3):223–9. doi: 10.1177/0003319712440143

  • CrossRef
  • Google Scholar

• 92

  PhamMNHawaMIRodenMSchernthanerGPozzilliPBuzzettiRet al. Increased serum concentrations of adhesion molecules but not of chemokines in patients with Type2 diabetes compared with patients with Type1 diabetes and latent autoimmune diabetes in adult age: Action LADA5. Diabetic Med (2012) 29(4):470–8. doi: 10.1111/j.1464-5491.2011.03546.x

  • CrossRef
  • Google Scholar

• 93

  PortaMAmioneCBaruttaFFornengoPMerloSGrudenGet al. The co-activator-associated arginine methyltransferase 1 (CARM1) gene is overexpressed in type 2 diabetes. Endocrine (2019) 63(2):284–92. doi: 10.1007/s12020-018-1740-z

  • CrossRef
  • Google Scholar

• 94

  PrechelMHudecSLowdenEEscalanteVEmanueleNEmanueleMet al. Profiling Heparin-Induced Thrombocytopenia (HIT) Antibodies in Hospitalized Patients With and Without Diabetes. Clin Appl Thromb Hemost (2018) 24(9_suppl):294S–300S. doi: 10.1177/1076029618808915

  • CrossRef
  • Google Scholar

• 95

  PushpanathanPSrikanthPSeshadriKGSelvarajanSPitaniRSKumarTDet al. Gut Microbiota in Type 2 Diabetes Individuals and Correlation with Monocyte Chemoattractant Protein1 and Interferon Gamma from Patients Attending a Tertiary Care Centre in Chennai, India. Indian J Endocrinol Metab (2016) 20(4):523–30. doi: 10.4103/2230-8210.183474

  • CrossRef
  • Google Scholar

• 96

  RuotsalainenEStancakovaAVauhkonenISalmenniemiUPihlajamakiJPunnonenKet al. Changes in cytokine levels during acute hyperinsulinemia in offspring of type 2 diabetic subjects. Atherosclerosis (2010) 210(2):536–41. doi: 10.1016/j.atherosclerosis.2009.11.036

  • CrossRef
  • Google Scholar

• 97

  SajadiSMAKhoramdelazadHHassanshahiGRafatpanahHHosseiniJMahmoodiMet al. Plasma Levels of CXCL1 (GRO-alpha) and CXCL10 (IP-10) are Elevated in Type 2 Diabetic Patients: Evidence for the Involvement of Inflammation and Angiogenesis/Angiostasis in this Disease State. Clin Lab (2013) 59(1-2):133–7. doi: 10.7754/Clin.Lab.2012.120225

  • CrossRef
  • Google Scholar

• 98

  SamarasKBotelhoNKChisholmDJLordRV. Subcutaneous and visceral adipose tissue gene expression of serum adipokines that predict type 2 diabetes. Obes (Silver Spring) (2010) 18(5):884–9. doi: 10.1038/oby.2009.443

  • CrossRef
  • Google Scholar

• 99

  SathishkumarCPrabuPBalakumarMLeninRPrabhuDAnjanaRMet al. Augmentation of histone deacetylase 3 (HDAC3) epigenetic signature at the interface of proinflammation and insulin resistance in patients with type 2 diabetes. Clin Epigenet (2016) 8:125. doi: 10.1186/s13148-016-0293-3

  • CrossRef
  • Google Scholar

• 100

  SaukkonenTMuttSJJokelainenJSaukkonenAMRazaGSKarhuTet al. Adipokines and inflammatory markers in elderly subjects with high risk of type 2 diabetes and cardiovascular disease. Sci Rep (2018) 8(1):12816. doi: 10.1038/s41598-018-31144-8

  • CrossRef
  • Google Scholar

• 101

  ShahRHinkleCCFergusonJFMehtaNNLiMQuLet al. Fractalkine is a novel human adipochemokine associated with type 2 diabetes. Diabetes (2011) 60(5):1512–8. doi: 10.2337/db10-0956

  • CrossRef
  • Google Scholar

• 102

  SindhuSAkhterNShenoudaSWilsonAAhmadR. Plasma fetuin-A/alpha 2-HS-glycoprotein correlates negatively with inflammatory cytokines, chemokines and activation biomarkers in individuals with type-2 diabetes. BMC Immunol (2016). doi: 10.1186/s12865-016-0171-y

  • CrossRef
  • Google Scholar

• 103

  SozerVHimmetogluSKorkmazGGKayaSAydinSYumukVet al. Paraoxonase, oxidized low density lipoprotein, monocyte chemoattractant protein-1 and adhesion molecules are associated with macrovascular complications in patients with type 2 diabetes mellitus. Minerva Med (2014) 105(3):237–44.

  • Google Scholar

• 104

  TavangarAKhozeimehFGhoreishianFBoroujeniMA. Serum level of Interleukin-8 in subjects with diabetes, diabetes plus oral lichen planus, and oral lichen planus: A biochemical study. Dent Res J (Isfahan) (2016) 13(5):413–8. doi: 10.4103/1735-3327.192277

  • CrossRef
  • Google Scholar

• 105

  TavangarAGhalayaniPBoroujeniMAGhoreishianFS. Salivary levels of interleukin-8 in oral lichen planus and diabetic patients: A biochemical study. Dent Res J (Isfahan) (2017) 14(3):209–14. doi: 10.4103/1735-3327.208771

  • CrossRef
  • Google Scholar

• 106

  ToanNLVan HoanNCuongDVDungNVDungPTHangNTet al. Adipose tissue-derived cytokines and their correlations with clinical characteristics in Vietnamese patients with type 2 diabetes mellitus. Diabetol Metab Syndr (2018) 10:41. doi: 10.1186/s13098-018-0343-4

  • CrossRef
  • Google Scholar

• 107

  TokarzADrozdzAESzuzcikIStzpiezE. Interplay of RANTES chemokine and CCR5+ bearing microvesicles in diabetic retinopathy. J Extracell Vesicles (2017) 6:119. doi: 10.1080/20013078.2017.1310414

  • CrossRef
  • Google Scholar

• 108

  TvarijonaviciuteACastilloCCeronJJMartinez-SubielaSTeclesFLopez-JornetP. Leptin and NGF in saliva of patients with diabetes mellitus type 2: A pilot study. J Oral Pathol Med (2017) 46(9):853–5. doi: 10.1111/jop.12587

  • CrossRef
  • Google Scholar

• 109

  UmapathyDDornadulaSKrishnamoorthyEMariappanadarVViswanathanVRamkumarKM. YKL-40: A biomarker for early nephropathy in type 2 diabetic patients and its association with inflammatory cytokines. Immunobiology (2018) 223(11):718–27. doi: 10.1016/j.imbio.2018.07.020

  • CrossRef
  • Google Scholar

• 110

  WadaTFuruichiKSakaiNIwataYYoshimotoKShimizuMet al. Up-regulation of monocyte chemoattractant protein-1 in tubulointerstitial lesions of human diabetic nephropathy. Kidney Int (2000) 58(4):1492–9. doi: 10.1046/j.1523-1755.2000.00311.x

  • CrossRef
  • Google Scholar

• 111

  WangXLiJWangZDengA. Wound exudate CXCL6: a potential biomarker for wound healing of diabetic foot ulcers. Biomarkers Med13(3):167–74. doi: 10.2217/bmm-2018-0339

  • CrossRef
  • Google Scholar

• 112

  WeiJTangQLiuLBinJ. Combination of peroxisome proliferator-activated receptor alpha/gamma agonists may benefit type 2 diabetes patients with coronary artery disease through inhibition of inflammatory cytokine secretion. Exp Ther Med5(3):783–8. doi: 10.3892/etm.2013.891

  • CrossRef
  • Google Scholar

• 113

  WuCWangQLvCQinNLeiSYuanQet al. The changes of serum sKlotho and NGAL levels and their correlation in type 2 diabetes mellitus patients with different stages of urinary albumin. Diabetes Res Clin Pract (2019) 106(2):343–50. doi: 10.1016/j.diabres.2014.08.026

  • CrossRef
  • Google Scholar

• 114

  XuJLiaoYFZhouWPMingHLWangQH. The MCP-1 Gene A-2518G Polymorphism Confers an Increased Risk of Vascular Complications in Type 2 Diabetes Mellitus Patients. Genet Testing Mol Biomarkers (2013) 19(8):411–7. doi: 10.1089/gtmb.2014.0325

  • CrossRef
  • Google Scholar

• 115

  YadavRHamaSLiuYSiahmansurTSchofieldJSyedAAet al. Effect of Roux-en-Y Bariatric Surgery on Lipoproteins, Insulin Resistance, and Systemic and Vascular Inflammation in Obesity and Diabetes. Front Immunol (2014) 8:1512. doi: 10.3389/fimmu.2017.01512

  • CrossRef
  • Google Scholar

• 116

  YangMShenZChenDGanHShenQYangBet al. Effects of 1,25-(OH)(2)D (3) on the expressions of vitamin D receptor, STAT5 and cytoskeletal rearrangement in human monocytes incubated with sera from type 2 diabetes patients and diabetic nephropathy patients with uremia. Inflammation Res (2012) 61(5):511–20. doi: 10.1007/s00011-012-0441-y

  • CrossRef
  • Google Scholar

• 117

  YiBHuXZhangHHuangJLiuJHuJet al. Nuclear NF-kappaB p65 in peripheral blood mononuclear cells correlates with urinary MCP-1, RANTES and the severity of type 2 diabetic nephropathy. PloS One (2014) 9(6):e99633. doi: 10.1371/journal.pone.0099633

  • CrossRef
  • Google Scholar

• 118

  ZengYCaoDYuHHuYHeMYangDet al. Comprehensive analysis of vitreous humor chemokines in type 2 diabetic patients with and without diabetic retinopathy. Acta Diabetol (2019) 56(7):797–805. doi: 10.1007/s00592-019-01317-6

  • CrossRef
  • Google Scholar

• 119

  ZhangJZhangZDingYXuPWangTXuWet al. Adipose Tissues Characteristics of Normal, Obesity, and Type 2 Diabetes in Uygurs Population. J Diabetes Res (2015) 2015:905042. doi: 10.1155/2015/905042

  • CrossRef
  • Google Scholar

• 120

  ZhouFWangJWangKZhuXPangRLiXet al. Serum CXCL16 as a Novel Biomarker of Coronary Artery Disease in Type 2 Diabetes Mellitus: a Pilot Study. Ann Clin Lab Sci (2016) 46(2):184–9.

  • Google Scholar

• 121

  AntonelliAFerriCFerrariSMFrascerraSRuffilliICaponiLet al. High levels of circulating chemokine (C-X-C motif) ligand 11 are associated with euthyroid or subclinically hypothyroid autoimmune thyroiditis and with chemokine (C-X-C motif) ligand 10. J Interferon Cytokine Res (2012) 32(2):74–80. doi: 10.1089/jir.2011.0051

  • CrossRef
  • Google Scholar

• 122

  AntonelliAFerrariSMFrascerraSDi DomenicantonioANicoliniAFerrariPet al. Increase of circulating CXCL9 and CXCL11 associated with euthyroid or subclinically hypothyroid autoimmune thyroiditis. J Clin Endocrinol Metab (2011) 96(6):1859–63. doi: 10.1210/jc.2010-2905

  • CrossRef
  • Google Scholar

• 123

  AntonelliAFerrariSMFrascerraSRuffilliIGelminiSMinutoMet al. Peroxisome proliferator-activated receptor-α agonists modulate CXCL9 and CXCL11 chemokines in Graves’ ophthalmopathy fibroblasts and preadipocytes. Mol Cell Endocrinol (2012) 349(2):255–61. doi: 10.1016/j.mce.2011.11.001

  • CrossRef
  • Google Scholar

• 124

  AntonelliAFerrariSMCorradoAFerranniniEFallahiP. Increase of interferon-γ inducible CXCL9 and CXCL11 serum levels in patients with active Graves’ disease and modulation by methimazole therapy. Thyroid (2013) 23(11):1461–9. doi: 10.1089/thy.2012.0485

  • CrossRef
  • Google Scholar

• 125

  FallahiPFerrariSMRagusaFRuffilliIEliaGPaparoSRet al. Th1 Chemokines in Autoimmune Endocrine Disorders. J Clin Endocrinol Metab (2000) 105(4). doi: 10.1210/clinem/dgz289

  • CrossRef
  • Google Scholar

• 126

  AntonelliAFerrariSMGiuggioliDFerranniniEFerriCFallahiP. Chemokine (C-X-C motif) ligand (CXCL)10 in autoimmune diseases. Autoimmun Rev (2014) 13(3):272–80. doi: 10.1016/j.autrev.2013.10.010

  • CrossRef
  • Google Scholar

• 127

  FallahiPFerrariSMCorradoAGiuggioliDFerriCAntonelliA. Targeting chemokine (C-X-C motif) receptor 3 in thyroid autoimmunity. Recent Pat Endocr Metab Immune Drug Discovery (2014) 8(2):95–101. doi: 10.2174/1872214808666140623114315

  • CrossRef
  • Google Scholar

• 128

  FallahiPFerrariSMEliaGNasiniFColaciMGiuggioliDet al. Novel Therapies for Thyroid Autoimmune Diseases. Expert Rev Clin Pharmacol (2016) 9(6):853–61. doi: 10.1586/17512433.2016.1157468

  • CrossRef
  • Google Scholar

• 129

  AntonelliAFerrariSMCorradoAFranceschiniSSGelminiSFerranniniEet al. Extra-ocular muscle cells from patients with Graves’ ophthalmopathy secrete α (CXCL10) and β (CCL2) chemokines under the influence of cytokines that are modulated by PPARγ. Autoimmun Rev (2014) 13(11):1160–6. doi: 10.1016/j.autrev.2014.08.025

  • CrossRef
  • Google Scholar

• 130

  PanXKamingaACWu WenSLiuA. Chemokines in post-traumatic stress disorder: A network meta-analysis. Brain Behav Immun (2020). doi: 10.1016/j.bbi.2020.11.033

  • CrossRef
  • Google Scholar

• 131

  PanXKamingaACWenSWLiuA. Chemokines in hepatocellular carcinoma: a meta-analysis. Carcinogenesis (2020). doi: 10.1093/carcin/bgaa106

  • CrossRef
  • Google Scholar

• 132

  BhatAANisarSMaachaSCarneiro-LoboTCAkhtarSSiveenKSet al. Cytokine-chemokine network driven metastasis in esophageal cancer; promising avenue for targeted therapy. Mol Cancer (2021) 20(1):2. doi: 10.1186/s12943-020-01294-3

  • CrossRef
  • Google Scholar

• 133

  PanXChiwanda KamingaALiuAWenSWChenJLuoJ. Chemokines in Non-alcoholic Fatty Liver Disease: A Systematic Review and Network Meta-Analysis. Front Immunol (2020) 11:1802. doi: 10.3389/fimmu.2020.01802

  • CrossRef
  • Google Scholar

• 134

  AkashMSRehmanKChenS. Role of inflammatory mechanisms in pathogenesis of type 2 diabetes mellitus. J Cell Biochem (2013) 114(3):525–31. doi: 10.1002/jcb.24402

  • CrossRef
  • Google Scholar

• 135

  MiraEBarberDFAlmonacidLZaballosASteinJVJJ. Statins Induce Regulatory T Cell Recruitment via a CCL1 Dependent Pathway1. (2008) 181: (5):3524–34. doi: 10.4049/jimmunol.181.5.3524

  • CrossRef
  • Google Scholar

• 136

  HotamisligilGSErbayE. Nutrient sensing and inflammation in metabolic diseases. Nat Rev Immunol (2008) 8(12):923–34. doi: 10.1038/nri2449

  • CrossRef
  • Google Scholar

• 137

  JagerJGrémeauxTCormontMLe Marchand-BrustelYTantiJF. Interleukin-1beta-induced insulin resistance in adipocytes through down-regulation of insulin receptor substrate-1 expression. Endocrinology148(1):241–51. doi: 10.1210/en.2006-0692

  • CrossRef
  • Google Scholar

• 138

  KernPARanganathanSLiCWoodLJ.G. Adipose tissue tumor necrosis factor and interleukin-6 expression in human obesity and insulin. J Physiol (2001). doi: 10.1152/ajpendo.2001.280.5.E745

  • CrossRef
  • Google Scholar

• 139

  KitadeHSawamotoKNagashimadaMInoueHYamamotoYSaiYet al. CCR5 plays a critical role in obesity-induced adipose tissue inflammation and insulin resistance by regulating both macrophage recruitment and M1/M2 status. Diabetes (2012) 61(7):1680–90. doi: 10.2337/db11-1506

  • CrossRef
  • Google Scholar

• 140

  SzalaiCCsászárACzinnerASzabóTFalusAJPR. Chemokine Receptor CCR2 and CCR5 Polymorphisms in Children with Insulin-Dependent Diabetes Mellitus. (1999) 46(1):82–4. doi: 10.1203/00006450-199907000-00014

  • CrossRef
  • Google Scholar

• 141

  GerhardtCCRomeroIACancelloRCamoinLStrosbergADJMEndocrinologyC. Chemokines control fat accumulation and leptin secretion by cultured human adipocytes. (2001) 175(1-2):81–92. doi: 10.1016/S0303-7207(01)00394-X

  • CrossRef
  • Google Scholar

• 142

  ChangTTChenJ. W. J. C. D.Emerging role of chemokine CC motif ligand 4 related mechanisms in diabetes mellitus and cardiovascular disease: friends or foes? (2016) 15(1):117. doi: 10.1186/s12933-016-0439-9

  • CrossRef
  • Google Scholar

• 143

  Barbé-TuanaFMKleinDIchiiHBermanDMCoffeyLKenyonNSet al. CD40-CD40 ligand interaction activates proinflammatory pathways in pancreatic islets. Diabetes (2006) 55(9):2437–45. doi: 10.2337/db05-1673

  • CrossRef
  • Google Scholar

• 144

  OmatsuTCepinskasGClarsonCPattersonEKAlharfiIMSummersKet al. CXCL1/CXCL8 (GRO alpha/IL-8) in human diabetic ketoacidosis plasma facilitates leukocyte recruitment to cerebrovascular endothelium in vitro. Am J Physiol-Endocrinol Metab (2014) 306(9):E1077–84. doi: 10.1152/ajpendo.00659.2013

  • CrossRef
  • Google Scholar

• 145

  StraczkowskiMDzienis-StraczkowskaSStêpieñAKowalskaISzelachowskaMKinalskaI. Plasma interleukin-8 concentrations are increased in obese subjects and related to fat mass and tumor necrosis factor-alpha system. J Clin Endocrinol Metab (2002) 87(10):4602–6. doi: 10.1210/jc.2002-020135

  • CrossRef
  • Google Scholar

• 146

  BruunJMLihnASMadanAKPedersenSBSchiøttKMFainJNet al. Higher production of IL-8 in visceral vs. subcutaneous adipose tissue. Implication of nonadipose cells in adipose tissue. Am J Physiol Endocrinol Metab (2004) 286(1):E8–13. doi: 10.1152/ajpendo.00269.2003

  • CrossRef
  • Google Scholar

• 147

  SchulthessFTParoniFSauterNSShuLRibauxPHaatajaLet al. CXCL10 Impairs β Cell Function and Viability in Diabetes through TLR4 Signaling. Cell Metab (2009) 9(2):125–39. doi: 10.1016/j.cmet.2009.01.003

  • CrossRef
  • Google Scholar

• 148

  TangYZhouHChenAPittmanRNFieldJ. The Akt proto-oncogene links Ras to Pak and cell survival signals. J Biol Chem (2000) 275(13):9106–9. doi: 10.1074/jbc.275.13.9106

  • CrossRef
  • Google Scholar

• 149

  BergmannKSypniewskaG. Secreted frizzled-related protein 4 (SFRP4) and fractalkine (CX3CL1) - Potential new biomarkers for β-cell dysfunction and diabetes. Clin Biochem (2014) 47(7-8):529–32. doi: 10.1016/j.clinbiochem.2014.03.007

  • CrossRef
  • Google Scholar

• 150

  CefaluWT. Fractalkine: a cellular link between adipose tissue inflammation and vascular pathologies. Diabetes (2011) 60(5):1380–2. doi: 10.2337/db11-0239

  • CrossRef
  • Google Scholar

• 151

  WanYEvansRM. Rosiglitazone activation of PPARgamma suppresses fractalkine signaling. J Mol Endocrinol (2010) 44(2):135–42. doi: 10.1677/jme-09-0090

  • CrossRef
  • Google Scholar

• 152

  van GenugtenREUtzschneiderKMTongJGerchmanFZraikaSUdayasankarJet al. Effects of sex and hormone replacement therapy use on the prevalence of isolated impaired fasting glucose and isolated impaired glucose tolerance in subjects with a family history of type 2 diabetes. Diabetes (2006) 55(12):3529–35. doi: 10.2337/db06-0577

  • CrossRef
  • Google Scholar

• 153

  Romero-FarinaGAguadé-BruixSCuellar-CalabriaHPizziMNRoqueACandell-RieraJ. Gender differences in outcome in patients with diabetes mellitus. J Nucl Cardiol (2020). doi: 10.1007/s12350-020-02195-0

  • CrossRef
  • Google Scholar

• 154

  Kautzky-WillerAHarreiterJPaciniG. Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus. Endocr Rev (2016) 37(3):278–316. doi: 10.1210/er.2015-1137

  • CrossRef
  • Google Scholar

• 155

  GalimbertiDScalabriniDFenoglioCDe RizMComiCVenturelliEet al. Gender-specific influence of the chromosome 16 chemokine gene cluster on the susceptibility to Multiple Sclerosis. J Neurol Sci (2008) 267(1-2):86–90. doi: 10.1016/j.jns.2007.10.001

  • CrossRef
  • Google Scholar

• 156

  ChenYVaughanRWKondeatisEFortuneFGrahamEMStanfordMRet al. Chemokine gene polymorphisms associate with gender in patients with uveitis. Tissue Antigens (2004) 63(1):41–5. doi: 10.1111/j.1399-0039.2004.00150.x

  • CrossRef
  • Google Scholar

• 157

  NagafusaHSayamaK. Age-related chemokine alterations affect IgA secretion and gut immunity in female mice. Biogerontology (2020). doi: 10.1007/s10522-020-09877-9

  • CrossRef
  • Google Scholar

• 158

  EngelbrechtCSardinhaLRRizzoLV. Cytokine and Chemokine Concentration in the Tear of Patients with Age-Related Cataract. Curr Eye Res (2020) 1–6. doi: 10.1080/02713683.2020.1715445

  • CrossRef
  • Google Scholar

• 159

  SeidlerSZimmermannHWBartneckMTrautweinCTackeF. Age-dependent alterations of monocyte subsets and monocyte-related chemokine pathways in healthy adults. BMC Immunol (2010) 11:30. doi: 10.1186/1471-2172-11-30

  • CrossRef
  • Google Scholar

• 160

  JaneschPStulikLRouhaHVargaCSteinhäuserJZerbsMet al. Age-related changes in the levels and kinetics of pulmonary cytokine and chemokine responses to Streptococcuspneumoniae in mouse pneumonia models. Cytokine (2018) 111:389–97. doi: 10.1016/j.cyto.2018.09.012

  • CrossRef
  • Google Scholar

• 161

  AntonelliARotondiMFallahiPRomagnaniPFerrariSMFerranniniEet al. Age-dependent changes in CXC chemokine ligand 10 serum levels in euthyroid subjects. J Interferon Cytokine Res (2005) 25(9):547–52. doi: 10.1089/jir.2005.25.547

  • CrossRef
  • Google Scholar

• 162

  YuanLLiuAQiaoLShengBXuMLiWet al. The relationship of CSF and plasma cytokine levels in HIV infected patients with neurocognitive impairment. BioMed Res Int (2015) 2015:506872. doi: 10.1155/2015/506872

  • CrossRef
  • Google Scholar

• 163

  CliffordVTebrueggeMZuffereyCGermanoSForbesBCosentinoLet al. Cytokine biomarkers for the diagnosis of tuberculosis infection and disease in adults in a low prevalence setting. Tuberculosis (Edinb) (2019) 114:91–102. doi: 10.1016/j.tube.2018.08.011

  • CrossRef
  • Google Scholar