The increased monocytic myeloid-derived suppressor cells in type 2 diabetes corelate with hyperglycemic and was a risk factor of infection and tumor

The increased monocytic myeloid-derived suppressor cells in type 2 diabetes corelate with hyperglycemic and was a risk factor of infection and tumor | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The increased monocytic myeloid-derived suppressor cells in type 2 diabetes corelate with hyperglycemic and was a risk factor of infection and tumor Ji Zhou, Mengjie Zhang, Xiaodi Ju, Huiping Wang, Xiao Hao, Zhimin Zhai, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2382115/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To explore the M-MDSCs frequency in T2DM patients and whether it is corelated to the glycaemia, infection and tumor development. Methods We recruited healthy volunteers and T2DM patients for this study. M-MDSCs frequency in the peripheral blood, FPG, HbA1c levels, and other relevant indicators were detected. T2DM patients were further divided into good glycaemic control (GGC) and poor control (PGC) groups, and each patient was followed up for at least 6 months after the M-MDSCs were tested. We then analysed and compared the M-MDSCs frequency in the healthy population to various subgroups of T2DM patients, as well as the associations between M-MDSCs, glycaemia, infection, and tumor development. Results The M-MDSCs frequency was significantly higher in T2DM patients with PGC than in the healthy population (2.54% vs 0.93%), but there was no significant difference between patients with GGC and the healthy group (P > 0.05). The M-MDSCs frequency was positively correlated with FPG and HbA1c levels (R = 0.517 and 0.315, respectively). In addition, the patients who had tumors had the highest M-MDSCs number (12.89%), vastly more than those in the patients who only had an infection (3.14%) and the patients who had neither infection nor tumor (1.95%). When M-MDSCs frequency was higher than 2.8% or 11.24%, the risk ratios for infection or tumor occurrence were 2.5-fold and 43.2-fold higher in T2DM patients, respectively. Conclusions Elevated M-MDSC levels are associated with hyperglycaemia and may be a useful indicator for predicting the risk of infection or tumor development in T2DM patients. Myeloid-derived suppressor cells Type 2 diabetes mellitus Hyperglycaemia Infection Tumor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction T2DM is a common metabolic disorder mainly caused by a combination of two factors: insulin secretion deficiency by pancreatic β-cells and the inability of insulin-sensitive tissues to appropriately respond to insulin, resulting in varying degrees of hyperglycaemia [ 1 – 3 ]. According to the International Diabetes Federation (IDF 2021), approximately 537 million adults (20–79 years) suffer from diabetes and 6.7 million patients died from diabetes in 2021. Type 2 diabetes (T2DM) is the most common type of diabetes, accounting for approximately 90% of all diabetes cases. Epidemiological data show that individuals with T2DM have a higher risk of developing infections or tumors [ 4 – 8 ]. Additionally, some data suggest that the immune system in T2DM patients is impaired, highlighting the need to study the relationship between immunity and glucose metabolism [ 9 – 10 ]. Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature myeloid cells that play a critical role in suppressing immune responses in various pathological settings [ 11 ]. MDSCs comprise two major subpopulations: monocytic MDSCs (M-MDSCs) and granulocytic MDSCs (G-MDSCs). M-MDSCs have a higher immunosuppressive capacity, and the current method of M-MDSC detection based on the phenotype is well established in humans [ 12 ]. Therefore, there is a relatively large body of research on the relationship between M-MDSCs and various clinical diseases. Evolving data suggest that elevated M-MDSCs in peripheral blood are often related to disease conditions, including tumors, infections, and chronic inflammation [ 13 – 18 ], but there are very few studies on M-MDSCs and T2DM. We speculate that M-MDSCs may also be involved in the progression and development of T2DM, so we conducted this clinical study to understand the associations between glycaemia, M-MDSCs, infection, and tumor development in T2DM patients. 2. Methods 2.1 Study population From May 2019 to June 2020, 102 healthy volunteers were recruited from the Health Examination Centre in Second Affiliated Hospital of Anhui Medical University as controls to establish a normal reference range of M-MDSCs in our laboratory. We enrolled 77 patients with T2DM admitted to the Department of Endocrinology of the Second Affiliated Hospital of Anhui Medical University (Fig. 1 ). All the controls and patients belonged to the same ethnicity (local Han population) and geography (residents of central Anhui province). T2DM diagnosis was based on the ADA standard (2018) [ 19 ]. All the patients received standard treatment according to the guidelines for the prevention and treatment of diabetes in China (2017 edition). 2.2 Detection of MDSCs 2.2.1 Sample preparation We collected 2–5 ml of peripheral blood from both healthy individuals and patients. The samples were anti-coagulated with EDTA and used to detect M-MDSC frequency within 4 h. We added 100 µl of the peripheral blood into tubes and mixed it with FITC-conjugated CD14-specific monoclonal antibodies (mAb), PE-conjugated HLA-DR mAb, APC-conjugated CD33 mAb, and PC7-conjugated CD45 mAb or with their appropriate isotype controls and incubated them in the dark for 15 min at room temperature. Next, the red blood cells were lysed using ammonium chloride solution, and samples were detected immediately using flow cytometer FC-500 and analysed using CXP 2.0 software (Beckman Coulter, USA). The above-mentioned mAbs specific for human surface antigens were purchased from Beckman Coulter Immunotech (Miami, FL, USA): Fitc-labelled CD14 (clone 116), Pe-labelled HLA-DR (clone B8.12.2), Apc-labelled anti-CD33 (clone 13B8.2), PC7-labelled CD45 (clone J.33), and their appropriate isotype controls. 2.2.2 Flow cytometry detection and analysis Cells with a CD14 + CD33 + CD45 + HLA-DR⁻/low phenotype were defined as M-MDSCs [ 11 ]. The frequency of M-MDSCs refers to the proportion of M-MDSCs in CD14 + monocytes of the peripheral blood. The specific detection and analysis methods are shown in Fig. 2 A–D. 2.3 Detection of the other routine indicators Other routine metabolic markers, such as fasting plasma glucose (FPG), haemoglobin A1c (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and blood cell counts, were all measured and reported by the Clinical Examination Centre of the hospital. 2.4 Clinical data collection and follow-up of patients Clinical data of patients with M-MDSCs were collected, such as age, body mass index (BMI), T2DM duration, disease history, and other related examination results. Patients with the following conditions were excluded: type 1 diabetes mellitus, active infection, history or the possibility of other autoimmune diseases, tumors, immunologic deficiency diseases, and other serious complications. All enrolled patients were divided into two subgroups according to their glucose levels and were followed up for at least 6 months after the detection of M-MDSCs. According to the ADA standards [ 20 ], patients with FPG > 7.2 mmol/L or HbAlc > 7.0% were defined as the poor glycaemic control group (PGC), while patients with FPG ≤ 7.2 mmol/L and HbAlc ≤ 7.0% were assigned to the good glycaemic control group (GGC). During the follow-up period, the diagnosis of patients with an infection or tumor was based on clinical manifestations, elevated inflammatory factors or tumor serological indicators, imaging, and pathological examination, and confirmed by a clinical specialist. 2.5 Statistical analysis All statistical analyses were performed using the SPSS version 22.0 software (SPSS, Inc., Chicago, USA). Quantitative data were presented as medians and quartiles [M(Q 25 ,Q 75 )]. Differences among groups were analysed using one-way or two-way ANOVA, followed by Student-Newman-Keuls (SNK) multiple range test or Tukey's multiple comparison test if the data followed a normal distribution. The data that did not comply with the above-mentioned conditions were analysed using the Mann–Whitney U test or the Kolmogorov–Smirnov test. Correlations were analysed using Spearman’s test. Receiver operating characteristic (ROC) curves were plotted for predictive analysis and cut-off values. Univariate logistic analysis was used to explore risk factors for the occurrence of infection or tumors. Chi-square and Fisher’s exact tests were used to compare the incidence and relative risk (RR) between groups. Statistical significance was set at P < 0.05. 3. Results 3.1 The frequency of M-MDSCs in healthy people A total of 102 healthy individuals, spanning 40–65 years of age, received M-MDSCs detection. There was no significant difference in age or sex distribution between healthy individuals and patients with diabetes (male: 46.07% vs 48.05%, P = 0.695; age: 53.17 vs 54.00, P = 0.787). The median M-MDSC frequency in healthy individuals was 0.93% (interquartile range, 0.54–1.20%). We calculated the normal reference range and set the upper limit as a cut-off value (2.75%) according to the 95th quantile in healthy people. If the frequency of M-MDSCs in a patient exceeded 2.75%, we defined this patient as having a high level of M-MDSCs. 3.2 The frequency of M-MDSCs in T2DM patients 3.2.1 The clinical characteristics of T2DM patients We included 77 patients in the study. At the time of enrolment, the median duration since the first diagnosis of T2DM was 114 months (range, 1 month to 22 years), and none of the patients had any clinical signs of active infection or tumor. Other clinical characteristics, such as TC, leukocyte counts, and their subsets were also analysed and compared. The data showed no significant difference between the GGC and PGC subgroups in T2DM duration, sex distribution, BMI, TG, TC, LDL-C, and HDL-C, except for blood glucose level and age (Table 1 ). Table 1 Clinical characteristics of T2DM patients Characteristics a All T2DM patients (n = 77) GGC (n = 17) PGC (n = 60) Normal value b FPG (mmol/L) 7.24 (5.76, 9.59) 5.26 (4.73, 5.88) 8.23 * (6.35, 10.03) 3.90 ~ 6.1 HbA1c(%) 8.70 (7.30, 10.30) 6.70 (6.40, 7.25) 9.55 * (7.88, 10.88) 4.0 ~ 6.0 Age (Years) 54.00 (48.00, 61.00) 49.00 (42.50, 58.00) 55.00 * (50.25, 61.00) No Gender, Male(%) 48.05%(n = 37) 47.09%(n = 8) 48.33%(n = 29) No BMI 24.25 (22.64, 26.13) 24.13 (23.23, 25.24) 24.43 (22.50, 26.19) 18.5 ~ 22.9 T2DM duration (Years) 9.50 (4.00, 10.75) 9.00 (1.50, 10.00) 10.00 (5.00, 12.00) No TG (mmol/l) 1.61 (1.06, 2.52) 1.51 (0.99, 2.03) 1.64 (1.19, 2.72) 0.56–1.70 TC (mmol/l) 4.64 (3.75, 5.46) 4.35 (3.63, 5.13) 4.66 (3.84, 5.47) 2.86–5.98 HDL-C (mmol/l) 0.99 (0.82, 1.18) 0.98 (0.80, 1.10) 1.00 (0.82, 1.21) 1.04 ~ 1.55 LDL-C (mmol/l) 2.85 (2.35, 3.37) 2.78 (2.27, 3.22) 2.86 (2.33, 3.49) 0 ~ 3.37 WBC(10 9 /L) 6.41 (5.76, 7.47) 6.32 (5.45, 8.57) 6.52 (5.86, 7.38) 3.50–9.50 Lymphocyte (10 9 /L) 1.96 (1.56, 2.21) 2.14 (1.92, 2.51) 1.86 (1.49, 2.13) 1.10–3.20 Neutrophil (10 9 /L) 3.81 (3.06, 4.93) 3.55 (3.06, 5.26) 3.90 (3.01, 4.92) 1.80–6.30 Monocyte (10 9 /L) 0.45 (0.37, 0.57) 0.49 (0.35, 0.55) 0.44 (0.37, 0.59) 0.1.-0.60 a The Data are expressed as median (interquartile range) . b The normal reference values were reported by the Clinical Examination Centre of the hospital. * Compared with the GGC patient group (P 7.2 mmol/L or HbA1c > 7.0%). 3.2.2 The M-MDSCs in the PGC subgroup significantly increased and correlated with the glycaemia The M-MDSC frequency in all T2DM patients was 2.32% (interquartile range, 1.29 to 4.21%), which was significantly higher than that in the healthy population (0.93%). However, when the patients were divided into GGC and PGC subgroups, the number of M-MDSCs in the PGC group was observably higher than that in the healthy population (2.54% vs 0.93%), with no significant difference between the GGC group and the healthy population (1.65% vs 0.93%) (Table 2 and Fig. 3 ). To further understand the factors involving with the M-MDSC frequency in T2DM patients, we analysed the correlation between M-MDSCs and age, FPG, HbA1c, LDL-C, and BMI and found that M-MDSCs had moderate and weak positive correlation with FPG and HbA1c, respectively, but no correlation with the other indicators (Fig. 4 ). Table 2 The M-MDSCs frequency in healthy people and T2DM patients Subjects M-MDSCs frequency a (%) Healthy population (n = 102) 0.93% (0.54, 1.20) All T2DM patients (n = 77) 2.32% (1.29, 4.21)* GGC (n = 17) 1.65% (0.79, 3.76) PGC (n = 60) 2.54% (1.39, 4.32)* a The Data are expressed as median (interquartile range). GGC , T2DM patients with good glycaemic control (FPG ≤ 7.2 mmol/L and HbA1c ≤ 7.0%); PGC , patients with poor glycaemic control (FPG > 7.2 mmol/L or HbA1c > 7.0%). *Compared with the healthy population (P < 0.001). The significance of the differences was assessed using non-parametric testing (Mann–Whitney U test). 3.3 The increased M-MDSCs in T2DM patients may pose a high risk of infection and tumor occurrence During the follow-up period (the median time was 7.6 months, ranges from 6 months to 9.5 months), we found that the incidence of infection and tumor in T2DM patients with high M-MDSCs was 48.57% and 11.42%, respectively, significantly higher than those in patients with normal M-MDSCs (19.05% and 0.00%, respectively) (Table 3 ). Table 3 The incidence of infection and tumor in T2DM patients with different M-MDSCs frequency during follow-up period Normal-MDSCs (n = 42) High-MDSCs (n = 35) P value Infection Urinary tract Respiratory tract Ocular infection Oral infection Intra-abdominal infection 8 4 4 0 0 0 17 7 4 2 2 2 0.006 Tumor Prostatic cancer Multiple myeloma Myelogenous leukemia Lung cancer 0 0 0 0 0 4 1 1 1 1 0.039 Normal-MDSCs , T2DM patients with normal M-MDSC frequency (< 2.75%); High-MDSCs , T2DM patients with high M-MDSC frequency (≥ 2.75%). The significance of the differences was assessed using the chi-square test or Fisher’s exact test. To further explore the association between M-MDSCs and infection or tumor development in T2DM patients, we analysed and compared MDSCs and other clinical indicators in T2DM patients with or without the development of infection or tumor. In group A (patients with tumors), the number of M-MDSCs was the highest and vastly higher than that in group B (patients who only experienced infection) and group C (patients without an infection or tumor). Group B M-MDSC levels were higher than that of group C (P < 0.05). Regarding the other indicators, only the FPG and lymphocyte counts showed some statistical differences. The FPG levels in groups A and B were significantly higher than those in group C, but there was no significant difference between groups A and B. In contrast, the lymphocyte counts in group A were significantly lower than those in groups B and C; however, there was no significant difference between the latter two (Table 4 ). On this basis, we specifically analysed whether M-MDSCs, FPG, and lymphocyte counts could predict the development of infection or tumor in patients with T2DM using a receiver operating characteristic curve (ROC) and univariate logistic analysis. For infection, M-MDSCs and FPG showed statistical significance (AUC = 0.705 and 0.704, respectively). For tumors, M-MDSCs and FPG also showed statistical significance (AUC = 0.89 and 0.798, respectively). There was no statistically significant difference in lymphocyte counts while predicting either infection or tumor. The best cut-off points of M-MDSCs for indicating the occurrence of infection and tumor were 2.80% and 11.24%, respectively, whereas the best cut-off point of FPG for showing infection and tumor occurrence was 8.91 mmol/L and 9.74 mmol/L, respectively. (Fig. 5 ). Using these cut-off points and univariate logistic analysis, the risk rate (RR) for tumor occurrence in patients with M-MDSCs > 11.24% and FPG > 9.74 mmol/L was 43.20 (95% CI: 5.432–343.63) and 10.58 (95% CI: 1.175–95.384). The RR for infection occurrence in patients with M-MDSCs > 2.8% and FPG > 8.91 mmol/L was 2.50 (95% CI: 1.235–5.060) and 2.84 (95% CI: 1.534–5.244), respectively. Table 4 Comparing of M-MDSCs, FPG and Lymphocyte count among patients with or without infection and tumor occurring Patient groups M-MDSCs frequency (%) FPG (mmol/L) Lymphocyte count (10 9 /L) group A a (n = 4) 12.89 *, # (5.35, 17.22) 10.66 # (8.05, 14.30) 0.63 *, # (0.37, 1.39) group B b (n = 22) 3.14 # (1.90, 6.58) 9.20 # (6.02, 11.58) 1.80 (1.17, 2.13) group C c (n = 51) 1.95 (1.13, 3.37) 6.45 (5.49, 8.70) 1.97 (1.69, 2.44) Data are expressed as median (interquartile range). a Group A included patients who had tumor (patients who developed both infection and tumor during follow-up were also included in this group). b Group B included patients who only experienced an infection. c Group C included patients who developed neither infections nor tumors. * P < 0.05, compared with group B. # P < 0.05, compared with group C. Statistical differences were assessed using non-parametric tests. 4. Discussion T2DM is a complex metabolic disorder, often accompanied by obesity, which is associated with autoimmune responses. Autoimmune responses might lead to pancreatic inflammation, disrupting the insulin system [ 21 – 26 ]. Hyperglycaemia not only affects the heart, vasculature, and kidneys but also conversely damages the immune system, leading to serious diseases, including an increased risk of infection and tumor [ 4 – 10 ]. Therefore, further understanding of the association between blood glucose levels and immune dysfunction in T2DM might help to control these diseases. M-MDSCs are a critical cell population that suppresses innate and adaptive immunity and promotes infection and tumor development [ 11 – 17 ]. We aimed to prove that M-MDSCs are associated with the development of T2DM and the higher incidence of infection and tumor in diabetic patients. Our results showed that M-MDSCs were significantly increased in T2DM patients with PGC, and had a positive correlation with FPG and HbA1c, implying that hyperglycaemia could induce the expansion of M-MDSCs. Li et al. recently reported that MDSCs were abnormally accumulated in diabetic mice. To investigate whether high glucose levels contributed to the accumulation of MDSCs, they co-cultured bone marrow cells from normal mice with IL-6, GM-CSF, and different concentrations of glucose to generate MDSCs in vitro , and observed that the induced MDSCs increased gradually with increasing glucose concentration. These data are very similar to our findings in T2DM patients and support that high glucose may promote the expansion of MDSCs. Li et al. demonstrated that activation of the mTOR signalling pathway plays a key role in the stimulation of MDSCs by high glucose, but we need to study it further to reveal the underlying mechanisms. Our prospective observational study showed that the development of infection and tumors in T2DM patients were associated with abnormally increased M-MDSCs. The frequency of M-MDSCs in the subgroup of T2DM patients who had tumors was the highest (12.89%), followed by the subgroup with infection (3.14%), and finally by the subgroup of patients who had neither infection nor tumor (1.95%). Interestingly, FPG displayed a similar trend (10.66, 9.20, and 6.45 mmol/L, respectively) and lymphocyte counts showed the opposite tendency (0.63, 1.80, and 1.97 × 10 9 /L). The other clinical indicators did not show a significant difference. Finally, we confirmed that a high M-MDSC number was a high-risk factor for T2DM patients to develop tumors and infections using ROC and univariate logistic analysis; however, lymphocyte count had no predictive implications for either. According to the gathered data, many studies have demonstrated that inflammation can activate MDSCs and drive their accumulation and suppressive activity [ 13 – 18 ], which also has important implications for the pathological mechanism of prediabetes and diabetes. Adipose tissue expansion and obesity can induce chronic low-grade inflammation, leading to insulin resistance, impaired insulin secretion, and ultimately, hyperglycaemia. Hyperglycaemia, in turn, can promote inflammation and MDSCs expansion [ 19 , 21 – 28 ]. However, published research investigating the relationship between M-MDSCs and T2DM is scarce. In 2018, Wang et al. reported that the frequency of M-MDSCs in the peripheral blood of both T2DM patients and diabetic mice was significantly increased, and MDSCs prevented the development of diabetes by inhibiting CD4 + T-cell activity [ 29 ]. Combining these results and data, we believe that M-MDSC expansion that is mainly stimulated by transient hyperglycaemia in prediabetes or the early stages of diabetes and obesity-related inflammation, may be beneficial in preventing the development of disease. However, an uncontrolled hyperglycaemic condition leads to a sustained high-glucose environment that persistently promotes M-MDSC accumulation, strongly suppressing the immunity. This may ultimately render patients at high risk for infection and tumors. Therefore, MDSCs are a critical immune factor during the development and progression of diabetes, and controlling hyperglycaemia as soon as possible and maintaining it in the normal range is very important to protect the immune system and prevent infection or tumors in T2DM patients [ 22 , 28 ]. Our research indicated that M-MDSC accumulation is associated with poor glycaemic control, and the M-MDSC level may be a useful marker for predicting the risk of infection or tumor development in T2DM patients. Abbreviations T2DM: Type 2 diabetes mellitus; M-MDSCs: Monocytic myeloid-derived suppressor cells; HDL-C: High density lipoprotein cholesterol; LDL-C :Low density lipoprotein cholesterol; TC: Total cholesterol; TG: Triglyceride; FPG: Fasting plasma glucose; HbA1c: Haemoglobin A1c; BMI: Body mass index Declarations Availability of data and materials This study does not include a dataset, so a data availability statement is not applicable. Funding The study was funded by the National Natural Science Foundation of China [grant number. 81573017], Research project of Chinese Nursing Association [grant number. ZHKY201812]. Competing Interests The authors have nothing to disclose. Author Contributions Jingfang Hong and Xing Zhong designed the study, supervised the research and reviewed/revised the manuscript. Jingfang Hong provided research fund. Ji Zhou and Hao Xiao performed the experiments. Ji Zhou and Mengjie Zhang analyzed and interpreted data, and wrote the draft of manuscript. Huiping Wang and Xiaodi Ju validate the data. All authors read and approved the manuscript for publication. Ethical approval The study protocol was approved by the Ethics Committee of the Anhui Medical University (approval number: YJ-YX2020-004). Written informed consent was obtained from all patients and volunteers. Consent to participate Informed consent was obtained from all individual participants included in the study. References Trojnar M, Patro-Małysza J, Kimber-Trojnar Ż, Leszczyńska-Gorzelak B, Mosiewicz J. Associations between Fatty Acid-Binding Protein 4⁻A Proinflammatory Adipokine and Insulin Resistance, Gestational and Type 2 Diabetes Mellitus. 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Gr-1+ CD11b+ myeloid-derived suppressor cells suppress inflammation and promote insulin sensitivity in obesity. J Biol Chem. 2011;286(26):23591-23599. doi:10.1074/jbc.M111.237123 Chang SC, Yang WV. Hyperglycemia, tumorigenesis, and chronic inflammation. Crit Rev Oncol Hematol. 2016;108:146-153. doi:10.1016/j. critrevonc.2016.11.003 Wang T, Wen Y, Fan X. Myeloid-derived suppressor cells suppress CD4+ T cell activity and prevent the development of type 2 diabetes. Acta Biochim Biophys Sin (Shanghai). 2018;50(4):362-369. doi:10.1093/abbs/gmy014 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2382115","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":160559468,"identity":"2e458bcb-3fc0-4c75-ab03-7e18a8f700d9","order_by":0,"name":"Ji Zhou","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ji","middleName":"","lastName":"Zhou","suffix":""},{"id":160559469,"identity":"f30f2b28-adde-4bbd-88a7-4d62684aac84","order_by":1,"name":"Mengjie Zhang","email":"","orcid":"","institution":"Fuyang People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mengjie","middleName":"","lastName":"Zhang","suffix":""},{"id":160559470,"identity":"73dfee34-cad5-45d4-afa5-179444848157","order_by":2,"name":"Xiaodi Ju","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaodi","middleName":"","lastName":"Ju","suffix":""},{"id":160559472,"identity":"9dbebfe3-cf0e-450c-83a2-1891e08b84c9","order_by":3,"name":"Huiping Wang","email":"","orcid":"","institution":"Second Hospital of Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huiping","middleName":"","lastName":"Wang","suffix":""},{"id":160559473,"identity":"6048268f-bd8e-438c-b225-08f275c9f065","order_by":4,"name":"Xiao Hao","email":"","orcid":"","institution":"Second Hospital of Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Hao","suffix":""},{"id":160559477,"identity":"80fdcf0f-fd95-4e94-8657-615fa3f856fc","order_by":5,"name":"Zhimin Zhai","email":"","orcid":"","institution":"Second Hospital of Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhimin","middleName":"","lastName":"Zhai","suffix":""},{"id":160559480,"identity":"c3dffde7-99bf-4b28-9ef3-1275270d2836","order_by":6,"name":"Xing Zhong","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Zhong","suffix":""},{"id":160559481,"identity":"23bfa4b7-5aea-409e-a3dd-9a39088fa0de","order_by":7,"name":"Jingfang Hong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3RLQ/CMBCA4SOXFFM2WwIBhy4hmeS3dFmCAoKcQEyQIRiZhYQfgUQWM1V85fAICAbFp4XQ4RB9dN9crwWwrD9E3PScH8NblyBucxGOzYnDpNdeKAzcchzwXGXmpAGC1yoxlpaJalX3EyxwMZCiQwlBrvsk9CMC7nQmvicYyf2KUofrQab9TR2Y2q0NU7YRPzD2mDLsaV8R4GxgSgJglPPSWve9kR9jkaQHVSrEc30PiiVMkfZCytcjM6EyatylmSaYn67y9ZWnSzhuuNP59+QN/e24ZVmW9dEdRC5KdZ/HfB0AAAAASUVORK5CYII=","orcid":"","institution":"Anhui Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jingfang","middleName":"","lastName":"Hong","suffix":""}],"badges":[],"createdAt":"2022-12-15 14:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2382115/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2382115/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":30532898,"identity":"1d1993d7-2dfc-4158-90cb-fe14f62e4b4d","added_by":"auto","created_at":"2022-12-19 19:18:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":599718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of subjects screening\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2382115/v1/40a752d3ce939d78f117c201.png"},{"id":30533076,"identity":"7b5a3ed4-223c-4a0c-b78a-d1aff8416bb2","added_by":"auto","created_at":"2022-12-19 19:26:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":302770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe detection of M-MDSCs in peripheral blood by flow cytometry.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM-MDSC was defined as CD14+CD33+CD45+HLA-DR⁻/low Monocyte. The analysis method as follows: Fig.2A: All karyocytes (Gate 1) were selected by SSC and CD45 expression from all events. Fig.2B: The CD33+CD45+ cells (Gate 2) were selected from Gate 1. Fig.2C: The CD14+/low Monocyte (Gate 3) were selected from Gate 2. Fig.2D: CD14+HLA-DR⁻/low Monocyte were selected from Gate 3 and the percentage of CD14+CD33+CD45+HLA-DR⁻/low Monocyte represents the M-MDSCs level. All data were acquired and analyzed by Software CytExpert (2.0).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2382115/v1/c3cc561beabfb52604432636.png"},{"id":30532900,"identity":"75e1b087-5f20-424d-8f5e-38e66d0a2b98","added_by":"auto","created_at":"2022-12-19 19:18:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe frequency of M-MDSCs in the healthy population and T2DM patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*Data were detected and analysed using FCM and CytExpert. The cells with a CD14+CD33+CD45+HLA-DR⁻/low phenotype were defined as M-MDSCs (red box gate). Our results show that higher M-MDSCs frequency in the peripheral blood of T2DM patients, especially those with hyperglycaemia, compared with healthy people.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2382115/v1/3383a40e41496454530ff1f3.png"},{"id":30533075,"identity":"494a1d5d-eeda-46a1-b5b3-0b51cb8a81a4","added_by":"auto","created_at":"2022-12-19 19:26:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29663,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between M-MDSCs and FPG, HbA1c, and Age\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*Data were analysed using Spearman’s correlation. There is a moderate positive correlation between M-MDSCs and FPG (Fig. 4A), a weak positive correlation between M-MDSCs and HbA1c (Fig. 4B), and no correlation between M-MDSCs and Age (Fig. 4C).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2382115/v1/6597f73469ba3800e27bbbf6.png"},{"id":30532901,"identity":"05695246-44b1-4eae-82df-9bd16a6fba99","added_by":"auto","created_at":"2022-12-19 19:18:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92067,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ROC curves of M-MDSCs and FPG for predicting the occurrence of infection or tumor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*Data were analysed by running the receiver operating characteristic (ROC). The area under the curve (AUC) and the best Youden index were calculated from the ROC curve. The results showed that M-MDSCs may be a significant biomarker for predicting the occurrence of infection and tumor, with optimal cut-off points of 2.80% and 11.24%, respectively, indicating their sensitivity and specificity (Fig. 5A, 5B). Again, FPG was significant for predicting infection and tumors. The optimal cut-off points of FPG for predicting infection and tumor was 8.91 mmol/L and 9.74 mmol/L, respectively.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2382115/v1/160685c82cb676da557e3dd0.png"},{"id":42193465,"identity":"99c89f7b-7a0e-4e8f-85eb-03542e7ac58f","added_by":"auto","created_at":"2023-08-27 08:52:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3445725,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2382115/v1/1196df33-ae7e-4e48-bcae-f220ab8e6a3e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The increased monocytic myeloid-derived suppressor cells in type 2 diabetes corelate with hyperglycemic and was a risk factor of infection and tumor","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eT2DM is a common metabolic disorder mainly caused by a combination of two factors: insulin secretion deficiency by pancreatic β-cells and the inability of insulin-sensitive tissues to appropriately respond to insulin, resulting in varying degrees of hyperglycaemia [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the International Diabetes Federation (IDF 2021), approximately 537\u0026nbsp;million adults (20\u0026ndash;79 years) suffer from diabetes and 6.7\u0026nbsp;million patients died from diabetes in 2021. Type 2 diabetes (T2DM) is the most common type of diabetes, accounting for approximately 90% of all diabetes cases. Epidemiological data show that individuals with T2DM have a higher risk of developing infections or tumors [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, some data suggest that the immune system in T2DM patients is impaired, highlighting the need to study the relationship between immunity and glucose metabolism [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMyeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature myeloid cells that play a critical role in suppressing immune responses in various pathological settings [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. MDSCs comprise two major subpopulations: monocytic MDSCs (M-MDSCs) and granulocytic MDSCs (G-MDSCs). M-MDSCs have a higher immunosuppressive capacity, and the current method of M-MDSC detection based on the phenotype is well established in humans [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, there is a relatively large body of research on the relationship between M-MDSCs and various clinical diseases. Evolving data suggest that elevated M-MDSCs in peripheral blood are often related to disease conditions, including tumors, infections, and chronic inflammation [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], but there are very few studies on M-MDSCs and T2DM. We speculate that M-MDSCs may also be involved in the progression and development of T2DM, so we conducted this clinical study to understand the associations between glycaemia, M-MDSCs, infection, and tumor development in T2DM patients.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eFrom May 2019 to June 2020, 102 healthy volunteers were recruited from the Health Examination Centre in Second Affiliated Hospital of Anhui Medical University as controls to establish a normal reference range of M-MDSCs in our laboratory. We enrolled 77 patients with T2DM admitted to the Department of Endocrinology of the Second Affiliated Hospital of Anhui Medical University (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All the controls and patients belonged to the same ethnicity (local Han population) and geography (residents of central Anhui province). T2DM diagnosis was based on the ADA standard (2018) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. All the patients received standard treatment according to the guidelines for the prevention and treatment of diabetes in China (2017 edition).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Detection of MDSCs\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Sample preparation\u003c/h2\u003e \u003cp\u003eWe collected 2\u0026ndash;5 ml of peripheral blood from both healthy individuals and patients. The samples were anti-coagulated with EDTA and used to detect M-MDSC frequency within 4 h. We added 100 \u0026micro;l of the peripheral blood into tubes and mixed it with FITC-conjugated CD14-specific monoclonal antibodies (mAb), PE-conjugated HLA-DR mAb, APC-conjugated CD33 mAb, and PC7-conjugated CD45 mAb or with their appropriate isotype controls and incubated them in the dark for 15 min at room temperature. Next, the red blood cells were lysed using ammonium chloride solution, and samples were detected immediately using flow cytometer FC-500 and analysed using CXP 2.0 software (Beckman Coulter, USA).\u003c/p\u003e \u003cp\u003eThe above-mentioned mAbs specific for human surface antigens were purchased from Beckman Coulter Immunotech (Miami, FL, USA): Fitc-labelled CD14 (clone 116), Pe-labelled HLA-DR (clone B8.12.2), Apc-labelled anti-CD33 (clone 13B8.2), PC7-labelled CD45 (clone J.33), and their appropriate isotype controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Flow cytometry detection and analysis\u003c/h2\u003e \u003cp\u003eCells with a CD14\u003csup\u003e+\u003c/sup\u003eCD33\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e HLA-DR⁻/low phenotype were defined as M-MDSCs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The frequency of M-MDSCs refers to the proportion of M-MDSCs in CD14\u003csup\u003e+\u003c/sup\u003e monocytes of the peripheral blood. The specific detection and analysis methods are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;D.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Detection of the other routine indicators\u003c/h2\u003e \u003cp\u003eOther routine metabolic markers, such as fasting plasma glucose (FPG), haemoglobin A1c (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and blood cell counts, were all measured and reported by the Clinical Examination Centre of the hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Clinical data collection and follow-up of patients\u003c/h2\u003e \u003cp\u003eClinical data of patients with M-MDSCs were collected, such as age, body mass index (BMI), T2DM duration, disease history, and other related examination results. Patients with the following conditions were excluded: type 1 diabetes mellitus, active infection, history or the possibility of other autoimmune diseases, tumors, immunologic deficiency diseases, and other serious complications. All enrolled patients were divided into two subgroups according to their glucose levels and were followed up for at least 6 months after the detection of M-MDSCs. According to the ADA standards [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], patients with FPG\u0026thinsp;\u0026gt;\u0026thinsp;7.2 mmol/L or HbAlc\u0026thinsp;\u0026gt;\u0026thinsp;7.0% were defined as the poor glycaemic control group (PGC), while patients with FPG \u0026le; 7.2 mmol/L and HbAlc \u0026le; 7.0% were assigned to the good glycaemic control group (GGC). During the follow-up period, the diagnosis of patients with an infection or tumor was based on clinical manifestations, elevated inflammatory factors or tumor serological indicators, imaging, and pathological examination, and confirmed by a clinical specialist.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using the SPSS version 22.0 software (SPSS, Inc., Chicago, USA). Quantitative data were presented as medians and quartiles [M(Q\u003csub\u003e25\u003c/sub\u003e,Q\u003csub\u003e75\u003c/sub\u003e)]. Differences among groups were analysed using one-way or two-way ANOVA, followed by Student-Newman-Keuls (SNK) multiple range test or Tukey's multiple comparison test if the data followed a normal distribution. The data that did not comply with the above-mentioned conditions were analysed using the Mann\u0026ndash;Whitney U test or the Kolmogorov\u0026ndash;Smirnov test. Correlations were analysed using Spearman\u0026rsquo;s test. Receiver operating characteristic (ROC) curves were plotted for predictive analysis and cut-off values. Univariate logistic analysis was used to explore risk factors for the occurrence of infection or tumors. Chi-square and Fisher\u0026rsquo;s exact tests were used to compare the incidence and relative risk (RR) between groups. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The frequency of M-MDSCs in healthy people\u003c/h2\u003e \u003cp\u003eA total of 102 healthy individuals, spanning 40\u0026ndash;65 years of age, received M-MDSCs detection. There was no significant difference in age or sex distribution between healthy individuals and patients with diabetes (male: 46.07% vs 48.05%, P\u0026thinsp;=\u0026thinsp;0.695; age: 53.17 vs 54.00, P\u0026thinsp;=\u0026thinsp;0.787). The median M-MDSC frequency in healthy individuals was 0.93% (interquartile range, 0.54\u0026ndash;1.20%). We calculated the normal reference range and set the upper limit as a cut-off value (2.75%) according to the 95th quantile in healthy people. If the frequency of M-MDSCs in a patient exceeded 2.75%, we defined this patient as having a high level of M-MDSCs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The frequency of M-MDSCs in T2DM patients\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 The clinical characteristics of T2DM patients\u003c/h2\u003e \u003cp\u003eWe included 77 patients in the study. At the time of enrolment, the median duration since the first diagnosis of T2DM was 114 months (range, 1 month to 22 years), and none of the patients had any clinical signs of active infection or tumor. Other clinical characteristics, such as TC, leukocyte counts, and their subsets were also analysed and compared. The data showed no significant difference between the GGC and PGC subgroups in T2DM duration, sex distribution, BMI, TG, TC, LDL-C, and HDL-C, except for blood glucose level and age (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of T2DM patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll T2DM patients (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePGC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNormal value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.24\u003c/p\u003e \u003cp\u003e(5.76, 9.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003cp\u003e(4.73, 5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.23 \u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(6.35, 10.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.90\u0026thinsp;~\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.70\u003c/p\u003e \u003cp\u003e(7.30, 10.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003cp\u003e(6.40, 7.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.55 \u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(7.88, 10.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0\u0026thinsp;~\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.00\u003c/p\u003e \u003cp\u003e(48.00, 61.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.00\u003c/p\u003e \u003cp\u003e(42.50, 58.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.00 \u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(50.25, 61.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, Male(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.05%(n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.09%(n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.33%(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.25\u003c/p\u003e \u003cp\u003e(22.64, 26.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.13\u003c/p\u003e \u003cp\u003e(23.23, 25.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.43\u003c/p\u003e \u003cp\u003e(22.50, 26.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.5\u0026thinsp;~\u0026thinsp;22.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM duration (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003cp\u003e(4.00, 10.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003cp\u003e(1.50, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003cp\u003e(5.00, 12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003cp\u003e(1.06, 2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003cp\u003e(0.99, 2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003cp\u003e(1.19, 2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u0026ndash;1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003cp\u003e(3.75, 5.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003cp\u003e(3.63, 5.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003cp\u003e(3.84, 5.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.86\u0026ndash;5.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.82, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.80, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(0.82, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u0026thinsp;~\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003cp\u003e(2.35, 3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003cp\u003e(2.27, 3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003cp\u003e(2.33, 3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;3.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003cp\u003e(5.76, 7.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.32\u003c/p\u003e \u003cp\u003e(5.45, 8.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003cp\u003e(5.86, 7.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.50\u0026ndash;9.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003cp\u003e(1.56, 2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003cp\u003e(1.92, 2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003cp\u003e(1.49, 2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u0026ndash;3.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003cp\u003e(3.06, 4.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003cp\u003e(3.06, 5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003cp\u003e(3.01, 4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80\u0026ndash;6.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003cp\u003e(0.37, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003cp\u003e(0.35, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003cp\u003e(0.37, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1.-0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e The Data are expressed as median (interquartile range) .\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e The normal reference values were reported by the Clinical Examination Centre of the hospital.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003e*\u003c/b\u003eCompared with the GGC patient group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, assessed by non-parametric testing, K\u0026ndash;S test).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eGGC\u003c/b\u003e, T2DM patients with good glycaemic control (FPG\u0026thinsp;\u0026le;\u0026thinsp;7.1 mmol/L and HbA1c\u0026thinsp;\u0026le;\u0026thinsp;7.0%); \u003cb\u003ePGC\u003c/b\u003e, T2DM patients with poor glycaemic control (FPG\u0026thinsp;\u0026gt;\u0026thinsp;7.2 mmol/L or HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;7.0%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 The M-MDSCs in the PGC subgroup significantly increased and correlated with the glycaemia\u003c/h2\u003e \u003cp\u003eThe M-MDSC frequency in all T2DM patients was 2.32% (interquartile range, 1.29 to 4.21%), which was significantly higher than that in the healthy population (0.93%). However, when the patients were divided into GGC and PGC subgroups, the number of M-MDSCs in the PGC group was observably higher than that in the healthy population (2.54% vs 0.93%), with no significant difference between the GGC group and the healthy population (1.65% vs 0.93%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). To further understand the factors involving with the M-MDSC frequency in T2DM patients, we analysed the correlation between M-MDSCs and age, FPG, HbA1c, LDL-C, and BMI and found that M-MDSCs had moderate and weak positive correlation with FPG and HbA1c, respectively, but no correlation with the other indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe M-MDSCs frequency in healthy people and T2DM patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM-MDSCs frequency \u003csup\u003ea\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy population (n\u0026thinsp;=\u0026thinsp;102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93% (0.54, 1.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll T2DM patients (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.32% (1.29, 4.21)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGC (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.65% (0.79, 3.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGC (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.54% (1.39, 4.32)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e The Data are expressed as median (interquartile range). \u003cb\u003eGGC\u003c/b\u003e, T2DM patients with good glycaemic control (FPG\u0026thinsp;\u0026le;\u0026thinsp;7.2 mmol/L and HbA1c\u0026thinsp;\u0026le;\u0026thinsp;7.0%); \u003cb\u003ePGC\u003c/b\u003e, patients with poor glycaemic control (FPG\u0026thinsp;\u0026gt;\u0026thinsp;7.2 mmol/L or HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;7.0%). *Compared with the healthy population (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The significance of the differences was assessed using non-parametric testing (Mann\u0026ndash;Whitney U test).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 The increased M-MDSCs in T2DM patients may pose a high risk of infection and tumor occurrence\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDuring the follow-up period (the median time was 7.6 months, ranges from 6 months to 9.5 months), we found that the incidence of infection and tumor in T2DM patients with high M-MDSCs was 48.57% and 11.42%, respectively, significantly higher than those in patients with normal M-MDSCs (19.05% and 0.00%, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eThe incidence of infection and tumor in T2DM patients with different M-MDSCs frequency during follow-up period\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal-MDSCs\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-MDSCs\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInfection\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUrinary tract\u003c/p\u003e \u003cp\u003eRespiratory tract\u003c/p\u003e \u003cp\u003eOcular infection\u003c/p\u003e \u003cp\u003eOral infection\u003c/p\u003e \u003cp\u003eIntra-abdominal infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor\u003c/b\u003e\u003c/p\u003e \u003cp\u003eProstatic cancer\u003c/p\u003e \u003cp\u003eMultiple myeloma\u003c/p\u003e \u003cp\u003eMyelogenous leukemia\u003c/p\u003e \u003cp\u003eLung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNormal-MDSCs\u003c/b\u003e, T2DM patients with normal M-MDSC frequency (\u0026lt;\u0026thinsp;2.75%); \u003cb\u003eHigh-MDSCs\u003c/b\u003e, T2DM patients with high M-MDSC frequency (\u0026ge;\u0026thinsp;2.75%). The significance of the differences was assessed using the chi-square test or Fisher\u0026rsquo;s exact test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further explore the association between M-MDSCs and infection or tumor development in T2DM patients, we analysed and compared MDSCs and other clinical indicators in T2DM patients with or without the development of infection or tumor. In group A (patients with tumors), the number of M-MDSCs was the highest and vastly higher than that in group B (patients who only experienced infection) and group C (patients without an infection or tumor). Group B M-MDSC levels were higher than that of group C (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Regarding the other indicators, only the FPG and lymphocyte counts showed some statistical differences. The FPG levels in groups A and B were significantly higher than those in group C, but there was no significant difference between groups A and B. In contrast, the lymphocyte counts in group A were significantly lower than those in groups B and C; however, there was no significant difference between the latter two (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On this basis, we specifically analysed whether M-MDSCs, FPG, and lymphocyte counts could predict the development of infection or tumor in patients with T2DM using a receiver operating characteristic curve (ROC) and univariate logistic analysis. For infection, M-MDSCs and FPG showed statistical significance (AUC\u0026thinsp;=\u0026thinsp;0.705 and 0.704, respectively). For tumors, M-MDSCs and FPG also showed statistical significance (AUC\u0026thinsp;=\u0026thinsp;0.89 and 0.798, respectively). There was no statistically significant difference in lymphocyte counts while predicting either infection or tumor. The best cut-off points of M-MDSCs for indicating the occurrence of infection and tumor were 2.80% and 11.24%, respectively, whereas the best cut-off point of FPG for showing infection and tumor occurrence was 8.91 mmol/L and 9.74 mmol/L, respectively. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Using these cut-off points and univariate logistic analysis, the risk rate (RR) for tumor occurrence in patients with M-MDSCs\u0026thinsp;\u0026gt;\u0026thinsp;11.24% and FPG\u0026thinsp;\u0026gt;\u0026thinsp;9.74 mmol/L was 43.20 (95% CI: 5.432\u0026ndash;343.63) and 10.58 (95% CI: 1.175\u0026ndash;95.384). The RR for infection occurrence in patients with M-MDSCs\u0026thinsp;\u0026gt;\u0026thinsp;2.8% and FPG\u0026thinsp;\u0026gt;\u0026thinsp;8.91 mmol/L was 2.50 (95% CI: 1.235\u0026ndash;5.060) and 2.84 (95% CI: 1.534\u0026ndash;5.244), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparing of M-MDSCs, FPG and Lymphocyte count among patients with or without infection and tumor occurring\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM-MDSCs frequency (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFPG\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egroup A \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.89 \u003csup\u003e\u003cb\u003e*, #\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(5.35, 17.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.66 \u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(8.05, 14.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63 \u003csup\u003e\u003cb\u003e*, #\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.37, 1.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egroup B \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.14 \u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.90, 6.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.20 \u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(6.02, 11.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003cp\u003e(1.17, 2.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egroup C \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003cp\u003e(1.13, 3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.45\u003c/p\u003e \u003cp\u003e(5.49, 8.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003cp\u003e(1.69, 2.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are expressed as median (interquartile range). \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Group A included patients who had tumor (patients who developed both infection and tumor during follow-up were also included in this group). \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Group B included patients who only experienced an infection. \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e Group C included patients who developed neither infections nor tumors. \u003cb\u003e*\u003c/b\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05, compared with group B. \u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05, compared with group C. Statistical differences were assessed using non-parametric tests.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eT2DM is a complex metabolic disorder, often accompanied by obesity, which is associated with autoimmune responses. Autoimmune responses might lead to pancreatic inflammation, disrupting the insulin system [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Hyperglycaemia not only affects the heart, vasculature, and kidneys but also conversely damages the immune system, leading to serious diseases, including an increased risk of infection and tumor [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, further understanding of the association between blood glucose levels and immune dysfunction in T2DM might help to control these diseases.\u003c/p\u003e \u003cp\u003eM-MDSCs are a critical cell population that suppresses innate and adaptive immunity and promotes infection and tumor development [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We aimed to prove that M-MDSCs are associated with the development of T2DM and the higher incidence of infection and tumor in diabetic patients. Our results showed that M-MDSCs were significantly increased in T2DM patients with PGC, and had a positive correlation with FPG and HbA1c, implying that hyperglycaemia could induce the expansion of M-MDSCs. Li et al. recently reported that MDSCs were abnormally accumulated in diabetic mice. To investigate whether high glucose levels contributed to the accumulation of MDSCs, they co-cultured bone marrow cells from normal mice with IL-6, GM-CSF, and different concentrations of glucose to generate MDSCs \u003cem\u003ein vitro\u003c/em\u003e, and observed that the induced MDSCs increased gradually with increasing glucose concentration. These data are very similar to our findings in T2DM patients and support that high glucose may promote the expansion of MDSCs. Li et al. demonstrated that activation of the mTOR signalling pathway plays a key role in the stimulation of MDSCs by high glucose, but we need to study it further to reveal the underlying mechanisms. Our prospective observational study showed that the development of infection and tumors in T2DM patients were associated with abnormally increased M-MDSCs. The frequency of M-MDSCs in the subgroup of T2DM patients who had tumors was the highest (12.89%), followed by the subgroup with infection (3.14%), and finally by the subgroup of patients who had neither infection nor tumor (1.95%). Interestingly, FPG displayed a similar trend (10.66, 9.20, and 6.45 mmol/L, respectively) and lymphocyte counts showed the opposite tendency (0.63, 1.80, and 1.97 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L). The other clinical indicators did not show a significant difference. Finally, we confirmed that a high M-MDSC number was a high-risk factor for T2DM patients to develop tumors and infections using ROC and univariate logistic analysis; however, lymphocyte count had no predictive implications for either.\u003c/p\u003e \u003cp\u003eAccording to the gathered data, many studies have demonstrated that inflammation can activate MDSCs and drive their accumulation and suppressive activity [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which also has important implications for the pathological mechanism of prediabetes and diabetes. Adipose tissue expansion and obesity can induce chronic low-grade inflammation, leading to insulin resistance, impaired insulin secretion, and ultimately, hyperglycaemia. Hyperglycaemia, in turn, can promote inflammation and MDSCs expansion [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26 CR27\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, published research investigating the relationship between M-MDSCs and T2DM is scarce. In 2018, Wang et al. reported that the frequency of M-MDSCs in the peripheral blood of both T2DM patients and diabetic mice was significantly increased, and MDSCs prevented the development of diabetes by inhibiting CD4\u003csup\u003e+\u003c/sup\u003e T-cell activity [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Combining these results and data, we believe that M-MDSC expansion that is mainly stimulated by transient hyperglycaemia in prediabetes or the early stages of diabetes and obesity-related inflammation, may be beneficial in preventing the development of disease. However, an uncontrolled hyperglycaemic condition leads to a sustained high-glucose environment that persistently promotes M-MDSC accumulation, strongly suppressing the immunity. This may ultimately render patients at high risk for infection and tumors. Therefore, MDSCs are a critical immune factor during the development and progression of diabetes, and controlling hyperglycaemia as soon as possible and maintaining it in the normal range is very important to protect the immune system and prevent infection or tumors in T2DM patients [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur research indicated that M-MDSC accumulation is associated with poor glycaemic control, and the M-MDSC level may be a useful marker for predicting the risk of infection or tumor development in T2DM patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eT2DM:\u003c/strong\u003e Type 2 diabetes mellitus;\u0026nbsp;\u003cstrong\u003eM-MDSCs:\u003c/strong\u003e Monocytic myeloid-derived suppressor cells;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHDL-C:\u003c/strong\u003e High density lipoprotein cholesterol;\u0026nbsp;\u003cstrong\u003eLDL-C\u003c/strong\u003e:Low density lipoprotein cholesterol;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTC:\u003c/strong\u003e Total cholesterol; \u003cstrong\u003eTG:\u003c/strong\u003e Triglyceride;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFPG:\u0026nbsp;\u003c/strong\u003eFasting plasma glucose; \u003cstrong\u003eHbA1c:\u003c/strong\u003e Haemoglobin A1c; \u003cstrong\u003eBMI:\u003c/strong\u003e Body mass index\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not include a dataset, so a data availability statement is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the National Natural Science Foundation of China [grant number. 81573017], Research project of Chinese Nursing Association [grant number. ZHKY201812].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJingfang Hong and Xing Zhong designed the study, supervised the research and reviewed/revised the manuscript. Jingfang Hong provided research fund. Ji Zhou and Hao Xiao performed the experiments. Ji Zhou and Mengjie Zhang analyzed and interpreted data, and wrote the draft of manuscript. Huiping Wang and Xiaodi Ju validate the data. All authors read and approved the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of the Anhui Medical University (approval number: YJ-YX2020-004). Written informed consent was obtained from all patients and volunteers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTrojnar M, Patro-Małysza J, Kimber-Trojnar Ż, Leszczyńska-Gorzelak B, Mosiewicz J. Associations between Fatty Acid-Binding Protein 4⁻A Proinflammatory Adipokine and Insulin Resistance, Gestational and Type 2 Diabetes Mellitus. Cells. 2019;8(3):227. doi:10.3390/ cells 8030227\u003c/li\u003e\n\u003cli\u003eRorsman P, Ashcroft FM. Pancreatic \u0026beta;-Cell Electrical Activity and Insulin Secretion: Of Mice and Men. Physiol Rev. 2018;98(1):117-214. doi:10.1152/ physrev.00008.2017\u003c/li\u003e\n\u003cli\u003eZheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. 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CA Cancer J Clin. 2010;60(4):207-221. doi:10.3322/caac.20078\u003c/li\u003e\n\u003cli\u003eAbudawood M. Diabetes and cancer: A comprehensive review. J Res Med Sci. 2019;24:94. doi:10.4103/jrms.JRMS_242_19\u003c/li\u003e\n\u003cli\u003eBerbudi A, Rahmadika N, Tjahjadi AI, Ruslami R. Type 2 Diabetes and its Impact on the Immune System. Curr Diabetes Rev. 2020;16(5):442-449. doi:10.2174/ 1573399815666191024085838\u003c/li\u003e\n\u003cli\u003eJafar N, Edriss H, Nugent K. The Effect of Short-Term Hyperglycemia on the Innate Immune System. Am J Med Sci. 2016;351(2):201-211. doi:10.1016/ j.amjms.2015.11.011\u003c/li\u003e\n\u003cli\u003eBronte V, Brandau S, Chen SH, et al. Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat Commun. 2016;7:12150. doi:10.1038/ncomms1215\u003c/li\u003e\n\u003cli\u003eBruger AM, Dorhoi A, Esendagli G, et al. How to measure the immunosuppressive activity of MDSC: assays, problems and potential solutions. Cancer Immunol Immunother. 2019;68(4):631-644. doi:10.1007/ s00262-018-2170-8\u003c/li\u003e\n\u003cli\u003eParker KH, Beury DW, Ostrand-Rosenberg S. Myeloid-Derived Suppressor Cells: Critical Cells Driving Immune Suppression in the Tumor Microenvironment. Adv Cancer Res. 2015;128:95-139. doi:10.1016/bs.acr.2015.04.002\u003c/li\u003e\n\u003cli\u003eMa P, Beatty PL, McKolanis J, Brand R, Schoen RE, Finn OJ. Circulating Myeloid Derived Suppressor Cells (MDSC) That Accumulate in Premalignancy Share Phenotypic and Functional Characteristics With MDSC in Cancer. Front Immunol. 2019;10:1401. doi:10.3389/fimmu.2019.01401\u003c/li\u003e\n\u003cli\u003eMedina E, Hartl D. Myeloid-Derived Suppressor Cells in Infection: A General Overview. J Innate Immun. 2018;10(5-6):407-413. doi:10.1159/000489830\u003c/li\u003e\n\u003cli\u003eDorhoi A, Glar\u0026iacute;a E, Garcia-Tellez T, et al. MDSCs in infectious diseases: regulation, roles, and readjustment. Cancer Immunol Immunother. 2019;68(4): 673-685. doi:10.1007/s00262-018-2277-y\u003c/li\u003e\n\u003cli\u003eDorhoi A, Du Plessis N. Monocytic Myeloid-Derived Suppressor Cells in Chronic Infections. Front Immunol. 2018;8:1895. Published 2018 Jan 4. doi:10.3389/ fimmu.2017.01895\u003c/li\u003e\n\u003cli\u003eMeyer C, Sevko A, Ramacher M, et al. Chronic inflammation promotes myeloid-derived suppressor cell activation blocking antitumor immunity in transgenic mouse melanoma model. Proc Natl Acad Sci U S A. 2011;108(41):17111-17116. doi:10.1073/pnas.1108121108\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care. 2018; 41(Suppl 1):S13-S27. doi:10.2337/dc18-S002\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association. 6. Glycemic Targets: Standards of Medical Care in Diabetes-2018. Diabetes Care. 2018;41(Suppl 1):S55-S64. doi:10.2337/dc18-S006\u003c/li\u003e\n\u003cli\u003eGalicia-Garcia U, Benito-Vicente A, Jebari S, et al. Pathophysiology of Type 2 Diabetes Mellitus. Int J Mol Sci. 2020;21(17):6275. doi:10.3390/ijms21176275\u003c/li\u003e\n\u003cli\u003eRoden M, Shulman GI. The integrative biology of type 2 diabetes. Nature. 2019;576(7785):51-60. doi:10.1038/s41586-019-1797-8\u003c/li\u003e\n\u003cli\u003ePrasad M, Chen EW, Toh SA, Gascoigne NRJ. Autoimmune responses and inflammation in type 2 diabetes. J Leukoc Biol. 2020;107(5):739-748. doi:10.1002/JLB.3MR0220-243R\u003c/li\u003e\n\u003cli\u003eSaltiel AR, Olefsky JM. Inflammatory mechanisms linking obesity and metabolic disease. J Clin Invest. 2017;127(1):1-4. doi:10.1172/JCI92035\u003c/li\u003e\n\u003cli\u003eLuc K, Schramm-Luc A, Guzik TJ, Mikolajczyk TP. Oxidative stress and inflammatory markers in prediabetes and diabetes. J Physiol Pharmacol. 2019;70(6):10.26402/jpp.2019.6.01. doi:10.26402/jpp.2019.6.01\u003c/li\u003e\n\u003cli\u003eOguntibeju OO. Type 2 diabetes mellitus, oxidative stress and inflammation: examining the links. Int J Physiol Pathophysiol Pharmacol. 2019;11(3):45-63. \u003c/li\u003e\n\u003cli\u003eXia S, Sha H, Yang L, Ji Y, Ostrand-Rosenberg S, Qi L. Gr-1+ CD11b+ myeloid-derived suppressor cells suppress inflammation and promote insulin sensitivity in obesity. J Biol Chem. 2011;286(26):23591-23599. doi:10.1074/jbc.M111.237123\u003c/li\u003e\n\u003cli\u003eChang SC, Yang WV. Hyperglycemia, tumorigenesis, and chronic inflammation. Crit Rev Oncol Hematol. 2016;108:146-153. doi:10.1016/j. critrevonc.2016.11.003\u003c/li\u003e\n\u003cli\u003eWang T, Wen Y, Fan X. Myeloid-derived suppressor cells suppress CD4+ T cell activity and prevent the development of type 2 diabetes. Acta Biochim Biophys Sin (Shanghai). 2018;50(4):362-369. doi:10.1093/abbs/gmy014\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Myeloid-derived suppressor cells, Type 2 diabetes mellitus, Hyperglycaemia, Infection, Tumor","lastPublishedDoi":"10.21203/rs.3.rs-2382115/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2382115/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo explore the M-MDSCs frequency in T2DM patients and whether it is corelated to the glycaemia, infection and tumor development.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe recruited healthy volunteers and T2DM patients for this study. M-MDSCs frequency in the peripheral blood, FPG, HbA1c levels, and other relevant indicators were detected. T2DM patients were further divided into good glycaemic control (GGC) and poor control (PGC) groups, and each patient was followed up for at least 6 months after the M-MDSCs were tested. We then analysed and compared the M-MDSCs frequency in the healthy population to various subgroups of T2DM patients, as well as the associations between M-MDSCs, glycaemia, infection, and tumor development.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe M-MDSCs frequency was significantly higher in T2DM patients with PGC than in the healthy population (2.54% vs 0.93%), but there was no significant difference between patients with GGC and the healthy group (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The M-MDSCs frequency was positively correlated with FPG and HbA1c levels (R\u0026thinsp;=\u0026thinsp;0.517 and 0.315, respectively). In addition, the patients who had tumors had the highest M-MDSCs number (12.89%), vastly more than those in the patients who only had an infection (3.14%) and the patients who had neither infection nor tumor (1.95%). When M-MDSCs frequency was higher than 2.8% or 11.24%, the risk ratios for infection or tumor occurrence were 2.5-fold and 43.2-fold higher in T2DM patients, respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eElevated M-MDSC levels are associated with hyperglycaemia and may be a useful indicator for predicting the risk of infection or tumor development in T2DM patients.\u003c/p\u003e","manuscriptTitle":"The increased monocytic myeloid-derived suppressor cells in type 2 diabetes corelate with hyperglycemic and was a risk factor of infection and tumor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-19 19:18:29","doi":"10.21203/rs.3.rs-2382115/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d09508f3-b60f-4592-90d5-72e6782af7f1","owner":[],"postedDate":"December 19th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-27T08:44:25+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-19 19:18:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2382115","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2382115","identity":"rs-2382115","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}