Diabetes is associated with increased cardiovascular risk and higher occurrence of infections. These complications suggest altered responses of the innate immune system. Recent studies have shown that energy metabolism of monocytes is crucial in determining their functionality. Here we investigate whether monocyte metabolism and function are changed in patients with diabetes and aim to identify diabetes-associated factors driving these alterations. Patients with type 1 diabetes (T1D) (n = 41) and healthy age-, sex-, and BMI-matched control subjects (n = 20) were recruited. Monocytes were isolated from peripheral blood to determine immune functionality, metabolic responses, and transcriptome profiles. Upon ex vivo stimulation with Toll-like receptor (TLR) 4 or TLR-2 agonists, monocytes of patients with T1D secreted lower levels of various cytokines and showed lower glycolytic rates compared with monocytes isolated from matched control subjects. Stratification based on HbA1c levels revealed that lower cytokine secretion was coupled to higher glycolytic rate of monocytes in patients with a higher glycemic burden. Circulating monocytes displayed an enhanced inflammatory gene expression profile associated with high glycemic burden. These results suggest that a high glycemic burden in patients with T1D is related to expression of inflammatory genes of monocytes and is associated with an impaired relationship between metabolism and inflammatory function upon activation.

The presence of diabetes is associated with an altered innate immune cell function (1,2). Diabetes is known to increase the risk for infection (3), suggestive of ineffective (innate) immune cell function, yet is also characterized by a chronic inflammatory state that contributes to the development of cardiovascular disease (CVD) driven by inflammatory monocytes and macrophages (4,5). In patients with diabetes, a number of systemic alterations occur, the most significant being varying levels of hyperglycemia and incidental hypoglycemia in patients treated with insulin. Also, dyslipidemia and changes in fibrinolytic factors and other metabolic changes related to obesity occur (6), mainly in type 2 diabetes (T2D). These systemic alterations have been associated with various complications. For example, an association between glycemic burden (HbA1c levels) and the risk for CVD has been well established (7). In addition to CVD, large cohort studies have demonstrated that the increased risk for infections in patients with diabetes is associated with HbA1c levels, age, presence of obesity, and the duration of diabetes (8,9).

On a functional level, various diabetes-associated alterations have been described in innate immune cells. Ex vivo studies using monocytes from patients with T2D link altered cytokine secretion to atherosclerotic complications (10,11). Furthermore, gene expression levels of proinflammatory cytokines interleukin (IL) 1 and IL-6 were elevated in unstimulated monocytes from patients with type 1 diabetes (T1D) or T2D (12). Other functional alterations of innate immune cells have also been observed, including impairments in ex vivo bacterial killing and phagocytic function of polymorphonuclear leukocytes (13).

Recent evidence from the field of immunology has established the relevance of innate immune cell metabolism in determining functional output. This is illustrated both by robust intracellular metabolic rewiring after encountering a pathogen and by specific metabolic signatures accompanying innate immune cell polarization (14). In monocytes, pathogenic stimulation promotes unique metabolic adaptations to build an effective response (15). One of the most prominent features of activated immune cells is activation of glycolysis. The metabolic rewiring relies on intracellular changes to increase the glycolytic rate, yet also depends on extracellular factors, including substrate availability. For example, increasing glucose availability by enhancing GLUT1 activity promotes a proinflammatory phenotype in macrophages (16). In diabetes, circulating monocytes are exposed to chronically elevated glucose concentrations (1), which can lead to functional changes (17), possibly by promoting alterations in their metabolic signature. A detailed assessment of changes in the function and metabolism of monocytes during diabetes and identification of potential factors that drive these alterations is currently lacking.

The purpose of this study was to investigate whether monocyte metabolism and function are changed in patients with diabetes and to identify diabetes-associated factors driving these alterations. We report that circulating monocytes from patients with T1D are characterized by a proinflammatory transcriptome. Upon stimulation ex vivo, however, the capacity to boost proinflammatory cytokine production is reduced, and an increased glycolytic rate after stimulation was seen in patients displaying a high glycemic burden.

Experimental Design

This study was a single-center cross-sectional case control study in patients with T1D and healthy control (HC) subjects. The study population consisted of three groups: patients with well-controlled T1D, patients with poorly controlled T1D, and HC subjects. The study was powered on the primary study outcome: vascular wall inflammation in the carotid and femoral arteries, quantified by 2-deoxy-2-[18F]fluoro-D-glucose–positron emission tomography (PET) scanning. One of the secondary outcomes was the ex vivo determination of the inflammatory/atherogenic phenotype and the cellular metabolism of circulating monocytes, the focus of this manuscript. The formal sample size calculation was based on the primary outcome and resulted in 20 subjects per group. Ethical approval for this study was obtained from the Radboud University Medical Center Institutional Review Board (NL62200.091.17 2017-3555) (Nijmegen, the Netherlands). The study was registered at ClinicalTrials.gov (NCT03441919) and was conducted according to the principles expressed in the Declaration of Helsinki. All participants gave written consent before participation.

Patients and Control Subjects

Included were 41 patients with T1D and 20 HC subjects without diabetes, carefully matched for age, sex, and BMI. Inclusion criteria for patients with diabetes were diagnosis based on clinical criteria, duration of diabetes ≥10 years, age ≥20 years to ≤60 years; group 1: HbA1c >64 mmol/mol, group 2: HbA1c ≤64 mmol/mol. Inclusion criteria for HC subjects were absence of disease, no use of medication, matched for age, sex, and BMI, and HbA1c <42 mmol/mol. Exclusion criteria for all groups were inability to provide informed consent, smoking, specific medication use (immunosuppressive drugs, use of statins <2 weeks before the PET-computed tomography, use of acetylsalicylic acid), previous cardiovascular events (ischemic stroke/transient ischemic attack, myocardial infarction, peripheral arterial disease), autoinflammatory or autoimmune diseases, current or recent infection (<3 months), previous vaccination (<3 months), renal failure (MDRD <45 mL/min per 1.73 m2), BMI >30 kg/m2, pregnancy or breast-feeding, claustrophobia, or severe hypoglycemia <1 week before PET-computed tomography. Patients included in the study were recruited from the outpatient clinic of the Radboud University Medical Center. Clinical characteristics are given in Table 1. HbA1c levels were averaged from the last 10 visits.

Table 1

Demographic and anthropometric characteristics of the subjects

T1DHCP value
Age, years 43 ± 13.2 (22–60) 43 ± 12.2 (23–59) 0.954 
Height, cm 175 ± 9 (159–198) 175 ± 9 (158–191) 0.764 
Weight, kg 76.3 ± 11.3 (53–100) 75.8 ± 14.4 (51–99) 0.874 
BMI, kg/m2 24.8 ± 2.7 (19.9–30.1) 24.7 ± 3.3 (18.7–29.9) 0.935 
Female, n (%) 21 (51) 10 (50) 0.930 
HbA1c, mmol/mol 64 ± 10 (39–91) 34 ± 4.3 (26–40) <0.00001 
Duration of T1D, years 27 ± 11.2 (10–47) —  
T1DHCP value
Age, years 43 ± 13.2 (22–60) 43 ± 12.2 (23–59) 0.954 
Height, cm 175 ± 9 (159–198) 175 ± 9 (158–191) 0.764 
Weight, kg 76.3 ± 11.3 (53–100) 75.8 ± 14.4 (51–99) 0.874 
BMI, kg/m2 24.8 ± 2.7 (19.9–30.1) 24.7 ± 3.3 (18.7–29.9) 0.935 
Female, n (%) 21 (51) 10 (50) 0.930 
HbA1c, mmol/mol 64 ± 10 (39–91) 34 ± 4.3 (26–40) <0.00001 
Duration of T1D, years 27 ± 11.2 (10–47) —  

Continuous variables are given in mean ± SD (range).

Cells and Culture

Peripheral blood mononuclear cells (PBMCs) were isolated by dilution of blood with pyrogen-free PBS and differential density centrifugation over Ficoll-Plaque gradient media (GE Healthcare, Eindhoven, the Netherlands). Obtained PBMCs were further purified to CD14+ monocytes using negative selection by depleting nonmonocyte cells using the classical monocyte isolation kit (Miltenyi Biotec B.V., Leiden, the Netherlands). Cells were resuspended in RPMI 1640 culture medium containing 11 mmol/L glucose (Invitrogen, Thermo Fisher Scientific, Eindhoven, the Netherlands) supplemented with 50 µg/mL gentamicin (Centraform), 2 mmol/L GlutaMAX (Gibco, Thermo Fisher Scientific), and 1 mmol/L pyruvate (Gibco, Thermo Fisher Scientific) (RPMI). For in vitro stimulation experiments, 1 × 105 CD14+ cells were cultured in 96-well flat bottom plates using RPMI medium containing 10% human pooled serum at 37°C and 5% CO2. CD14+ monocytes were stimulated for 24 h with 10 µg/mL Pam3CysK (P3C), 10 ng/mL Escherichia coli lipopolysaccharide (LPS) (serotype 055:B5) (Sigma-Aldrich, Zwijndrecht, the Netherlands), or only RPMI as control. Supernatants were stored at −20°C for future batchwise ELISA analyses.

Transcriptome Analysis

Total RNA was isolated from monocytes that were directly frozen at −80°C in TRIzol reagent (Invitrogen, Thermo Fisher Scientific) after isolation and was purified with the RNeasy Micro kit (Qiagen, Venlo, the Netherlands). For microarray analysis, the integrity of the RNA was verified with RNA 6000 Nano Chips using an Agilent 2100 Bioanalyzer (Agilent Technologies, Amstelveen, the Netherlands). RNA was considered suitable for array hybridization with an RNA integrity number of minimally 7. Per sample, 100 ng of purified RNA was labeled with the Whole-Transcript Sense Target Assay kit (P/N 900652; Affymetrix, Santa Clara, CA), which was hybridized to Affymetrix GeneChip Human Gene 2.1 ST arrays (Affymetrix). Hybridization, washing, and scanning of the peg arrays were done on the Affymetrix GeneTitan platform according to the manufacturer’s instructions.

The quality control and data analysis pipeline have been described in detail previously (18). Briefly, normalized expression estimates of probe sets were computed by the robust multiarray analysis (RMA) algorithm (19) as implemented in the Bioconductor library oligo (20). Probe sets were redefined using current genome information according to Dai et al. (21), based on annotations provided by the Entrez Gene database, which resulted in the profiling of 29,635 unique genes (custom CDF v23). Differentially expressed probe sets (genes) were identified by using linear models (limma) and an intensity-based moderated t statistic (22,23). The heterogeneity in gene expression profiles that was observed in both study groups was taken into account by fitting a heteroskedastic model that included relative quality weights that were computed for each sample per group (24,25). P values were corrected for multiple testing according to Benjamini and Hochberg (26). Probe sets that satisfied the criterion of a false discovery rate <0.01 were considered to be significantly regulated. Functional analysis of the transcriptome data were performed using Ingenuity Pathway Analysis (Qiagen).

Real-Time PCR

For gene expression analysis by real-time PCR, cDNA was generated by reverse transcription of 400 ng purified RNA with the iScript cDNA synthesis kit (Bio-Rad Laboratories, Hercules, CA), following the manufacturer’s instructions. Nucleic acid quantification and purity was assessed by using the NanoDrop spectrophotometer (Thermo Fisher Scientific). Gene expression analysis was performed in duplicate with SYBR Green reactions using the SensiMix (BioLine, London, U.K.) protocol with the CFX384 Touch Real-Time detection system (Bio-Rad Laboratories). Negative template controls did not show any amplification. CFX Maestro software (Bio-Rad Laboratories) was used to analyze quantitative PCR data, and cycle quantification values were calculated with the use of a calibration curve. Primers that were used for real-time PCR analysis are listed in Supplementary Table 1, together with measured efficiency and R2 of calibration curves. Expression of B2M and 36B4 was assessed as housekeeping genes. Expression of B2M was stable (average cycle quantification: 18.97, SD 0.36) and was used for normalization.

Mitochondrial Respiration and Glycolytic Rate

Directly after isolation, CD14+ monocytes were seeded in XF96 microplates (Agilent Technologies, Amstelveen, the Netherlands) (2 × 105 cells per well in quintuple) in RPMI 1640 medium containing 11 mmol/L glucose and rested for 1 h at 37°C and 5% CO2. Thereafter, cells were incubated in nonbuffered DMEM without glucose (D5030; Sigma-Aldrich), supplemented with 1 or 2 mmol/L l-glutamine (Sigma-Aldrich) in a CO2-free incubator (37°C) for 45 min. Measurements of oxygen consumption and extracellular acidification were performed at 37°C using a Seahorse XF96 Extracellular Flux Analyzer (Agilent Technologies). The Seahorse XF Cell Glyco Stress Test kit was used to characterize the dynamics of the glycolytic rate according to the extracellular acidification rate (ECAR). The cells were treated according to the manufacturers’ protocol with glucose (11 mmol/L), oligomycin (1 μmol/L), 2-DG (22 mmol/L) at the time points indicated in Fig. 2A. For measurement of different dynamics in mitochondrial oxygen consumption rate (OCR), the Seahorse XF Cell Mito Stress Test kit was used, with slight adjustments to the protocol. Cells were first treated with glucose (11 mmol/L), then oligomycin (1 μmol/L), and FCCP (1 μmol/L) in combination with pyruvate (1 mmol/L) and lastly rotenone ((1.25 μmol/L) and antimycin A (2.5 μmol/L) at the time points indicated in Fig. 2B. To assess parameters of OCR and ECAR during pathogenic stimulation, cells were injected with glucose (11 mmol/L), then P3C (10 µg/mL) or LPS (10 ng/mL) at the time points indicated in Fig. 2J and M.

Cytokine Measurements

Cytokine concentrations in supernatants were determined by ELISAs using commercially available kits for tumor necrosis factor-α (TNF-α) and IL-6, IL-1β, IL-1Ra, and IL-8 (R&D Systems, Bio-Techne, Minneapolis, MN).

Statistical Analysis

Participant characteristics were analyzed by one-way ANOVA using SPSS Statistics 25 software (IBM Corp, Armonk, NY) after normality was assessed using Q-Q plots. Stratification was based on calculated quartiles of the data. For parts of the analysis, clinical HbA1c cutoff points were used. While these cutoffs are biologically relevant and valid for this study, they may hamper statistical power. Data in Table 1 are presented as mean ± SD. Data are shown as box plots with median and spread (whiskers), or as graphs and bar plots with means ± SDs. In box plots and bar plots, all single data points are visualized. For all data, normality was assessed by generating Q-Q plots, using the qnorm function with R software. Significance was assessed using the Mann-Whitney U test when comparing two groups and the Kruskal-Wallis test when comparing more than two groups, followed by the Dunn test for multiple comparisons. Correlations were assessed by calculating Spearman ρ. A two-sided P value <0.05 was considered statistically significant. All data were visualized and analyzed using Prism 5.0 or 8.0 software (GraphPad Software, San Diego, CA) or RStudio (PBC, Boston, MA; https://www.rstudio.com/).

Data and Resource Availability

The data sets generated during the current study are available from the corresponding author upon reasonable request. The microarray data set that was generated and analyzed during the current study has been submitted to the Gene Expression Omnibus repository (GEO number: GSE154609). No applicable resources were generated or analyzed during the current study.

Demographics of the subjects included in the study are summarized in Table 1. HC subjects and patients with T1D were matched for age and BMI. The average duration of diabetes was 27.3 years. HbA1c levels in patients with T1D ranged from 39 mmol/mol (5.8%) to 91 mmol/mol (10.5%), with an average value of 63.4 mmol/mol (7.9%). In HC subjects, the average HbA1c value was 34 mmol/mol (5.2%) (Table 1).

Inflammatory Response of Monocytes Is Reduced in Patients With T1D Compared With HC Subjects

To test the inflammatory function of monocytes, cells were exposed to a Toll-like receptor (TLR) 2 agonist (P3C) and a TLR-4 (LPS) agonist to induce activation, and cytokine secretion was used as final readout. Although a robust induction of cytokines was observed in response to P3C and LPS both in patients with T1D and HC subjects (Fig. 1), the production of TNF-α, IL-1β, and IL-1Ra was significantly lower in patients with T1D (Fig. 1A, D–F, I, and J). Also the secretion of IL-6 tended to be reduced upon P3C stimulation and was lower upon LPS stimulation (Fig. 1B and G) in patients with T1D compared with HC subjects. Not all cytokines were affected: IL-8 secretion by monocytes was not different between patients with T1D and HC subjects after P3C or LPS stimulation.

Figure 1

Inflammatory cytokine secretion in patients with T1D and HC subjects. CD14+ monocytes were stimulated for 24 h with P3C or LPS. TNF-α (A, P = 0.0054 and F, P = 0.0098), IL-6 (B, P = 0.055 and G, P = 0.0269), IL-8 (C and H), IL-1β (D, P = 0.0048 and I, P = 0.0137), and IL-1Ra (E, P = 0.0159 and J, P = 0.0015) were determined in cell supernatants. HC subjects, n = 20; T1D patients, n = 41. *P < 0.05, **P < 0.01.

Figure 1

Inflammatory cytokine secretion in patients with T1D and HC subjects. CD14+ monocytes were stimulated for 24 h with P3C or LPS. TNF-α (A, P = 0.0054 and F, P = 0.0098), IL-6 (B, P = 0.055 and G, P = 0.0269), IL-8 (C and H), IL-1β (D, P = 0.0048 and I, P = 0.0137), and IL-1Ra (E, P = 0.0159 and J, P = 0.0015) were determined in cell supernatants. HC subjects, n = 20; T1D patients, n = 41. *P < 0.05, **P < 0.01.

Metabolism of Monocytes From Patients With T1D Differs Compared With HC Subjects

Directly after isolation, metabolism of the monocytes was measured with the Agilent Seahorse Extracellular Flux Analyzer using Seahorse XF Cell Glyco- and Cell Mito- Stress tests (Fig. 2A and B). No differences in maximal glycolytic capacity (Fig. 2C) or glycolytic reserve (Fig. 2D) existed between monocytes from patients with T1D and HC subjects. Also measurements of oxygen consumption revealed no differences in maximal respiration (Fig. 2E), and spare respiratory capacity (Fig. 2F) of monocytes from patients with T1D and HC subjects. Combining all glycolytic extracellular flux measurements at baseline and after glucose injection (Fig. 2G) revealed that basal glycolytic rates did not differ (Fig. 2H) but were significantly lower after glucose injection in monocytes derived from patients with T1D compared with cells from HC subjects (Fig. 2I).

Figure 2

Glycolytic and oxidative metabolism in monocytes from patients with T1D and HC subjects. Extracellular flux analysis was performed on monocytes isolated from patients with T1D and HC subjects, measuring ECARs and OCRs. Glycolysis (Glyco) stress tests, mitochondrial (Mito) stress tests, and acute stimulations were performed. The injection time points are indicated with dotted lines. Extracellular flux curves are shown for glycolytic activity (A) (Glyco stress test, ECAR) and respiratory activity (B) (Mito stress test, OCR). Glycolytic capacity (C) and glycolytic reserve (D) were calculated based on the Glyco stress test. Maximal respiratory capacity (E) and spare respiratory capacity (F) were calculated based on the Mito stress test. Effects of pathogenic stimulations on cellular metabolism were tested by first injecting glucose (GI for ECAR, P = 0.0355; N and O for OCR) and afterward P3C (J and K for ECAR, M and P for OCR) or LPS (L for ECAR, Q for OCR). Values are presented as means from three consecutive measurements within 20 min for baseline and glucose and as means from 13 consecutive measurements within 90 min for pathogenic stimulations. HC subjects: n = 20, T1D: n = 41. *P < 0.05.

Figure 2

Glycolytic and oxidative metabolism in monocytes from patients with T1D and HC subjects. Extracellular flux analysis was performed on monocytes isolated from patients with T1D and HC subjects, measuring ECARs and OCRs. Glycolysis (Glyco) stress tests, mitochondrial (Mito) stress tests, and acute stimulations were performed. The injection time points are indicated with dotted lines. Extracellular flux curves are shown for glycolytic activity (A) (Glyco stress test, ECAR) and respiratory activity (B) (Mito stress test, OCR). Glycolytic capacity (C) and glycolytic reserve (D) were calculated based on the Glyco stress test. Maximal respiratory capacity (E) and spare respiratory capacity (F) were calculated based on the Mito stress test. Effects of pathogenic stimulations on cellular metabolism were tested by first injecting glucose (GI for ECAR, P = 0.0355; N and O for OCR) and afterward P3C (J and K for ECAR, M and P for OCR) or LPS (L for ECAR, Q for OCR). Values are presented as means from three consecutive measurements within 20 min for baseline and glucose and as means from 13 consecutive measurements within 90 min for pathogenic stimulations. HC subjects: n = 20, T1D: n = 41. *P < 0.05.

We also determined the metabolic response toward P3C and LPS stimulations, which are both known to result in acute and unique metabolic adaptations of monocytes (15). Glycolytic activity and mitochondrial respiration of CD14+ monocytes were measured after P3C or LPS injection and monitored over the 1st h after stimulation (Fig. 2J and M and Supplementary Fig. 1). Stimulation with P3C increased glycolytic activity approximately twofold (Fig. 2K compared with Fig. 2H and I) and induced a subtle increase in OCR with similar responses in patients with T1D and HC subjects (Fig. 2P, compared with Fig. 2N and O). LPS injection had a smaller acute impact on glycolytic activity (Fig. 2L, compared with Fig. 2H and I) and mitochondrial respiration (Fig. 2Q, compared with Fig. 2N and O) in both groups compared with P3C. However, no differences in OCR or ECAR after pathogenic stimulation were observed between monocytes derived from patients with T1D and HC subjects.

A High Glycemic Burden Is Associated With Lower Cytokine Secretion

To identify factors contributing to altered responses in monocytes from patients with T1D, we determined the impact of diabetes duration and glucose control (HbA1c) on monocyte functionality. Stratification based on duration of T1D revealed no major impact on cytokine secretion of TLR-activated monocytes from patients with longer duration of T1D (Supplementary Fig. 2). To determine the effect of glucose control, cytokine levels upon TLR stimulation were stratified by HbA1c (Fig. 3A–E and Supplementary Fig. 3A–E), using HbA1c quartiles. High glycemic burden, reflected by high HbA1c levels, was associated with reduced monocyte function after P3C stimulation in patients with the highest HbA1c levels (71–91 mmol/mol; 8.6–10.5%) compared with HC subjects, illustrated by significantly reduced secretion levels of TNF-α and IL-1β (Fig. 3A and D) but not IL-6, IL-8, and IL-1Ra levels (Fig. 3B, C, and E). Similar patterns for TNF-α and a significant effect for IL-1Ra were found after LPS stimulation (Supplementary Fig. 3A and E).

Figure 3

Monocytes of patients with higher HbA1c levels have lower cytokine secretion. CD14+ monocytes were stimulated with P3C and TNF-α, IL-6, IL-8, IL-1β, and IL-1Ra were measured. Quartiles of equal size were created for patients with T1D based on HbA1c levels (A, P = 0.0084; BD, P = 0.0478; E), and two groups with clinically relevant HbA1c levels (≤53 and ≥75 mmol/mol) were compared against HC subjects (F, P = 0.002; GI, P = 0.0389; J, P = 0.0428). HC subjects: n = 20; T1D HbA1c quartiles: n = 10–11; HbA1c ≤53 mmol/mol: n = 7, HbA1c ≥75: n = 6. *P < 0.05, **P < 0.01.

Figure 3

Monocytes of patients with higher HbA1c levels have lower cytokine secretion. CD14+ monocytes were stimulated with P3C and TNF-α, IL-6, IL-8, IL-1β, and IL-1Ra were measured. Quartiles of equal size were created for patients with T1D based on HbA1c levels (A, P = 0.0084; BD, P = 0.0478; E), and two groups with clinically relevant HbA1c levels (≤53 and ≥75 mmol/mol) were compared against HC subjects (F, P = 0.002; GI, P = 0.0389; J, P = 0.0428). HC subjects: n = 20; T1D HbA1c quartiles: n = 10–11; HbA1c ≤53 mmol/mol: n = 7, HbA1c ≥75: n = 6. *P < 0.05, **P < 0.01.

By comparing the low and high ends of the glycemic spectrum, using clinically relevant cutoff values of ≤53 mmol/mol (7.0%, target value of treatment in clinical practice) and ≥75 mmol/mol (9%, uncontrolled glucose levels with high risk of long-term complications), we found reduced secretion of TNF-α, IL-1β, and IL-1Ra in patients with HbA1c levels ≥75 but not ≤53 mmol/mol, compared with HC subjects (Fig. 3F, I, and J). No differences in IL-6 and IL-8 secretion between patients and control subjects were observed using these cutoff values (Fig. 3G and H). Similar patterns for TNF-α and IL-1Ra were found after LPS stimulation (Supplementary Fig. 3F and J).

Altered Metabolic Activity During a High Glycemic Burden

Changes in functionality of monocytes were primarily associated to HbA1c levels but not to duration of diabetes. Similarly, duration of diabetes was not associated with differences in metabolic parameters of monocytes (Supplementary Fig. 4). Next, we set out to determine the relevance of glycemic burden as a driver for metabolic alterations of the cells. Stratifying for HbA1c levels, we observed comparable patterns for the various metabolic readouts related to glycolysis and oxidative phosphorylation. A nonsignificant, J-shaped association between different parameters of monocyte metabolism and glycemic burden was observed, as shown in Fig. 4A–J. This association is mostly illustrated by the metabolic readouts related to ECAR, including maximal glycolytic capacity (Fig. 4A) and response to P3C (Fig. 4E) or LPS (Supplementary Fig. 5). Here, a decreased glycolytic rate in patients in the lower quartiles of HbA1c (between 39 and 70 mmol/mol) can be seen compared with HC subjects, in addition to an increase in glycolytic rate in patients with high HbA1c levels. Using the HbA1c classifications of ≤53 mmol/mol and ≥75 mmol/mol led to similar results. As shown in Fig. 4F and H, the glycolytic rate was significantly higher in monocytes comparing HbA1c levels ≥75 mmol/mol versus levels ≤53 mmol/mol, although both groups were not significantly different from the HC subjects.

Figure 4

The association of HbA1c levels with monocyte metabolism. Extracellular flux of monocytes isolated from patients with T1D and HC subjects was measured. Values from patients with T1D are shown grouped into equally sized quartiles based on HbA1c levels and compared with values from HC subjects on glycolytic capacity (A), maximal respiratory capacity (B), ECAR at baseline (C), ECAR after glucose injection (D), or ECAR after P3C stimulation (E). Values from patients with T1D that fall within two groups of extremes are compared with values for HC subjects on glycolytic capacity (P = 0.0062) (F), maximal respiratory capacity (P = 0.0328) (G), ECAR at baseline (P = 0.0328) (H), ECAR after glucose injection (I), or ECAR after P3C stimulation (J). HbA1c quartiles; HC subjects: n = 20, T1D quartiles: n = 10–11, HbA1c ≤53 mmol/mol: n = 7, HbA1c ≥75 mmol/mol: n = 6. *P < 0.05, **P < 0.01.

Figure 4

The association of HbA1c levels with monocyte metabolism. Extracellular flux of monocytes isolated from patients with T1D and HC subjects was measured. Values from patients with T1D are shown grouped into equally sized quartiles based on HbA1c levels and compared with values from HC subjects on glycolytic capacity (A), maximal respiratory capacity (B), ECAR at baseline (C), ECAR after glucose injection (D), or ECAR after P3C stimulation (E). Values from patients with T1D that fall within two groups of extremes are compared with values for HC subjects on glycolytic capacity (P = 0.0062) (F), maximal respiratory capacity (P = 0.0328) (G), ECAR at baseline (P = 0.0328) (H), ECAR after glucose injection (I), or ECAR after P3C stimulation (J). HbA1c quartiles; HC subjects: n = 20, T1D quartiles: n = 10–11, HbA1c ≤53 mmol/mol: n = 7, HbA1c ≥75 mmol/mol: n = 6. *P < 0.05, **P < 0.01.

Because glycolytic metabolism and function of monocytes are closely intertwined and we observed significant differences in glycolytic responses of the monocytes between T1D and HC (Fig. 2I), we determined the association between glycolysis and cytokine production. To do so, we calculated ratios between ECAR and cytokine secretion (Fig. 5A–E). Patients with a low glycemic burden (HbA1c ≤53 mmol/mol) and HC subjects had similar ratios. However, in patients with a high glycemic burden (HbA1c ≥75 mmol/mol), glycolysis-to-cytokine ratios were significantly increased for TNF-α, IL-1β, and IL-1Ra but not for IL-6 and IL-8 secretion upon P3C stimulation compared with HC subjects and patients with a low glycemic burden (Fig. 5A–E). For OCR-to-cytokine secretion ratios, similar patterns were found (Supplementary Fig. 6A–E). Visualization of the correlation analysis between glycolytic parameters (ECAR and lactate production) and cytokine production versus HbA1c levels further illustrates the altered association between metabolism and function dependent on the glycemic burden (Fig. 5E) where in the higher end of glycemia, an increased metabolic rate is coupled to lower production of TNF-α.

Figure 5

Monocytes of patients with poor glycemic control have higher ECAR-to-cytokine secretion ratios. Extracellular flux analysis was measured in monocytes isolated from patients with T1D and HC subjects, measuring ECARs for 90 consecutive min after direct stimulation with P3C. Cytokine secretion was measured after 24-h stimulation with P3C. A ratio between ECAR and cytokine secretion was calculated and is shown for TNF-α (P = 0.0021) (A), IL-6 (B), IL8 (C), IL-1β (P = 0.008) (D), and IL-1Ra (P = 0.0195) (E). F: The correlation between ECAR (acute), TNF-α secretion (24 h), HbA1c, and lactate secretion (24 h) is shown for monocytes isolated from patients with T1D treated with P3C. HbA1c ≤53 mmol/mol: n = 7, HbA1c ≥75 mmol/mol: n = 6; correlation: n = 41. *P < 0.05, **P < 0.01.

Figure 5

Monocytes of patients with poor glycemic control have higher ECAR-to-cytokine secretion ratios. Extracellular flux analysis was measured in monocytes isolated from patients with T1D and HC subjects, measuring ECARs for 90 consecutive min after direct stimulation with P3C. Cytokine secretion was measured after 24-h stimulation with P3C. A ratio between ECAR and cytokine secretion was calculated and is shown for TNF-α (P = 0.0021) (A), IL-6 (B), IL8 (C), IL-1β (P = 0.008) (D), and IL-1Ra (P = 0.0195) (E). F: The correlation between ECAR (acute), TNF-α secretion (24 h), HbA1c, and lactate secretion (24 h) is shown for monocytes isolated from patients with T1D treated with P3C. HbA1c ≤53 mmol/mol: n = 7, HbA1c ≥75 mmol/mol: n = 6; correlation: n = 41. *P < 0.05, **P < 0.01.

Transcriptome Analysis of Circulating Monocytes From HC Subjects Versus Patients With T1D

To investigate the potential underlying mechanisms, we analyzed the transcriptome of monocytes directly after isolation to obtain insights into reprogramming of circulating monocytes during diabetes that may translate into altered responses upon activation. Because function and metabolism of monocytes were both impacted by HbA1c, we compared patients with the highest HbA1c levels (n = 12) with HC subjects (n = 12). The two groups did not differ regarding BMI, sex, and age. A heat map of the most differentially regulated genes (top 50 up- or downregulated with an adjusted P value <0.01) revealed upregulation of several inflammatory genes in the monocytes from patients with T1D versus HC subjects, including JUN, CXCL8 (IL-8), FOSB, and CXCL5 (Fig. 6A and B). This was corroborated by Ingenuity pathway analysis identifying enrichment of the IL-17A–induced signaling/activation pathway in monocytes from patients with T1D compared with HC subjects (Fig. 6C). The most significantly upregulated gene was JUN (c-JUN/AP1), a transcription factor known for mediating inflammatory responses (27,28). To further confirm these results, we measured the gene expression of proinflammatory genes JUN and CXCL5 by real-time PCR in monocytes obtained from patients with a low glycemic burden (HbA1c ≤53 mmol/mol) and a high glycemic burden (HbA1c levels ≥75 mmol/mol). As shown, expression of JUN (Fig. 6D) and CXCL5 (Fig. 6E) mRNA are significantly enhanced in monocytes from patients with a high glycemic burden compared with HC subjects.

Figure 6

Monocytes of patients with higher HbA1c levels show a proinflammatory gene expression signature. Gene expression levels were measured in mRNA isolated from CD14+ monocytes of patients with T1D and HC subjects by means of microarray. A: Relative fold changes of the top 50 up- (red) or downregulated (blue) genes are shown for patients with T1D vs. HC subjects (only genes with a P value <0.01 are shown). B: Ingenuity pathway analyses revealed the pathways that are most changed in patients with T1D vs. HC subjects. C: Inflammatory response heat map containing fold changes of the highest up- or downregulated genes in patients with T1D vs. HC subjects based on the Ingenuity disease analysis. Quantitative PCR analysis of JUN (P = 0.0413) (D) and CXCL5 (P = 0.0345) (E) in monocytes of HC subjects, and patients with HbA1c ≤53 mmol/mol (n = 7) or HbA1c ≥75 mmol/mol (n = 6). HC subjects: n = 12; T1D: n = 12.

Figure 6

Monocytes of patients with higher HbA1c levels show a proinflammatory gene expression signature. Gene expression levels were measured in mRNA isolated from CD14+ monocytes of patients with T1D and HC subjects by means of microarray. A: Relative fold changes of the top 50 up- (red) or downregulated (blue) genes are shown for patients with T1D vs. HC subjects (only genes with a P value <0.01 are shown). B: Ingenuity pathway analyses revealed the pathways that are most changed in patients with T1D vs. HC subjects. C: Inflammatory response heat map containing fold changes of the highest up- or downregulated genes in patients with T1D vs. HC subjects based on the Ingenuity disease analysis. Quantitative PCR analysis of JUN (P = 0.0413) (D) and CXCL5 (P = 0.0345) (E) in monocytes of HC subjects, and patients with HbA1c ≤53 mmol/mol (n = 7) or HbA1c ≥75 mmol/mol (n = 6). HC subjects: n = 12; T1D: n = 12.

Our results demonstrate that circulating monocytes from patients with T1D display a proinflammatory gene expression signature compared with HC subjects, which translates into a reduced reserve capacity (lower cytokine production) upon acute TLR stimulation. These changes appear to be mainly associated to the glycemic burden. In parallel, the higher glycolytic activity in monocytes from patients with T1D also associates with high glycemic burden. Altogether these results suggest that a high glycemic burden links to a proinflammatory signature in circulating monocytes that upon acute stimulation translates into a lower functional response and may explain both susceptibility to infections as well as enhanced vascular inflammation in the diabetic state.

In the current study, the release of several cytokines by monocytes of patients with T1D differed from HC subjects, with some specificity regarding the affected cytokines. The production and secretion of various cytokines is differentially regulated and therefore, the diabetic state may only impact on specific pathways. Previous studies have also demonstrated alterations in cytokine secretion of innate immune cells from patients with diabetes. Ex vivo studies performed with total mononuclear cells from patients with T1D showed similar reductions in IL-1β secretion upon LPS stimulation (29), although no reduction in TNF-α or IL-1Ra secretion was found (29,30). The latter observation is in contrast to our results, yet might be explained by the fact that these studies used a mixture of mononuclear cells from male patients with T1D, whereas we specifically measured monocyte responses from both male and female patients with T1D. Although total mononuclear cells might more closely resemble the situation in vivo, variations in cellular composition between study participants may hamper interpretation of the results. Furthermore, this setup enables us to specifically address the functional changes in monocytes. Our results revealed an altered response of monocytes to ex vivo stimulation with inflammatory stimuli characterized by a reduced capacity to produce cytokines, which appears to be linked to a high glycemic burden in patients with T1D. Other studies previously looking at monocyte function in T1D found inconsistent results on cytokine release, reporting either lower IL-1 and IL-6 (31) or higher IL-6 release in monocytes from patients with T1D (32). However, none of these studies stratified the data for HbA1c level or coupled the functional data to extensive metabolic phenotyping of the cells.

Optimal glycemic control is the main treatment goal to reduce long-term complications in the management of T1D (3335). Our results link poor glycemic control to aberrant monocyte functioning, which may both contribute to accelerated vascular damage and to an increased infection rate. From a mechanistic point of view, multiple explanations for our observation exist. For once, several biochemical pathways are known to increase oxidative stress mediated by hyperglycemia (3639), which has already been coupled to the metabolic activation of monocytes in patients with T2D (40). Although these pathways have mainly been studied in the context of microvascular complications, such as retinopathy and neuropathy, similar pathways might be altered in monocytes residing in hyperglycemic conditions, promoting functional changes. However, the transcriptomics analyses did not reveal major differences in oxidative pathways in monocytes of patients with T1D versus HC subjects. Since the glycolytic rate of innate immune cells is known to determine functional output, including cytokine production (41), changes in glucose availability as a direct substrate for monocytes might also explain our observations. Without stratifying for HbA1c, we observed a general decrease in the glycolytic rate ex vivo after the addition of glucose in monocytes derived from patients with T1D versus HC subjects. Because the glycolytic rate has been proposed as one of the main drivers of monocyte/macrophage activation (42), these metabolic changes may explain the reduced cytokine secretion.

Interestingly, our transcriptome analysis of circulating monocytes revealed an enhanced inflammatory signature associated to a high glycemic load. One of the most differentially regulated genes is JUN, which is known to be activated in LPS-stimulated human monocytes (43). Although these results may seem in contrast to the reduced cytokine levels upon ex vivo stimulation, they are in accordance with the presence of immune tolerance. It has been well established that LPS can induce tolerance with a robust initial response, followed by a nonresponsive cell upon a second exposure to LPS (44). A similar phenomenon may occur in monocytes exposed to the chronic diabetic milieu. Possibly, the hyperglycemic environment in vivo leads to chronic activation of monocytes, explaining the upregulated JUN mRNA expression in circulating monocytes from patients with T1D in the current study. This chronic activation translates into a lower reserve for the immune response upon a second activation ex vivo, due to immune tolerance.

The enrichment of the IL-17A signaling pathway, which included JUN upregulation, has been previously identified in the context of T1D. In a comparison of the transcriptome of mononuclear cells of patients with T1D to those of healthy individuals, Li et al. (45) found new molecular dynamic clusters that were highly enriched in the IL-17A pathway. It could be argued that the inflammatory changes found are related to autoimmunity involved in the pathogenesis of T1D (46), yet the specific increase in patients with a high glycemic load compared with those individuals with lower HbA1c levels points to an important role of glucose in regulating these pathways.

Although glycolytic rate after glucose injection was significantly lower in monocytes from patients with T1D compared with HC subjects, stratification based on glycemic load revealed a specific pattern with lower baseline and maximal glycolytic activity in well-controlled patients and higher baseline and maximal glycolytic activity in patients with a high glycemic burden (HbA1c ≥75 mmol/mol). In the group of patients with high glycemic burden, the higher basal and maximal glycolytic activity was coupled to lower cytokine secretion. Possibly, the monocytes attempt to compensate for low cytokine secretion by upregulation of their metabolic rate. Importantly, this effect seems only to be apparent in patients with a high glycemic burden, suggesting that monocytes can tolerate hyperglycemia up to a certain extent. Another potential explanation could be glucose desensitization of monocytes from patients with T1D, although this process cannot be demonstrated or excluded based on the current study.

One could speculate that the metabolic signature of monocytes is not the main determinant of cytokine release, but instead controls different functional properties of the cell that are known to be altered during T1D, including phagocytosis (47) and adhesion (32,48,49) Furthermore, the subtle changes in glycolytic rate and oxidative phosphorylation imply other pathways beyond metabolism controlling alterations in function including cytokine secretion. Alternatively, metabolic pathways other than H+ production (ECAR) or oxygen consumption (OCR), the parameters that are measured by extracellular flux, might be changed. Recently, metabolomic analyses performed with mononuclear cells derived from children susceptible to T1D development revealed decreased intracellular levels of lipid species and other polar metabolites (50). Possibly, changes in intracellular fluxes of specific metabolites that are induced by T1D development might also impact on the function of monocytes, leading to changed cytokine secretion levels. We were not able to observe robust metabolic differences in the transcriptomics analyses (measured before stimulation), suggesting that the metabolic changes only become apparent after a pathogenic trigger and might not yet be obvious in unstimulated circulating monocytes.

The results of this study elicit several important questions that would require follow-up studies. Firstly, it would be important to establish whether the changes we have observed in monocytes from patients with T1D are reversible; in other words, could better glycemic control revert the changes in function and metabolism observed in patients with high HbA1c levels? Secondly, since monocytes function as precursor cells for macrophages, it would be interesting to investigate whether alterations in monocytes, linked to a high glycemic load, also translate into alterations in resulting macrophages after differentiation.

In summary, our results show that circulating monocytes in the diabetic state display an inflammatory gene expression signature that is coupled to higher glycolytic activity and lower cytokine production upon activation. In patients with T1D, it seems that a high glycemic burden is linked to an altered ratio between metabolism and function of monocytes that may ultimately contribute to the development of various diabetes-associated complications.

Clinical trial reg. no. NCT03441919, clinicaltrials.gov

K.T., X.A.M.H.v.D., and A.W.M.J. are joint first authors.

C.J.T. and R.S. share joint senior authorship.

This article contains supplementary material online at https://doi.org/10.2337/figshare.12985445.

Acknowledgments. The authors thank Anneke Hijmans (Radboud University Medical Center, Nijmegen, the Netherlands) and Jenny Jansen (Wageningen University, Wageningen, the Netherlands) for their technical assistance.

Funding. This work was funded by the Dutch Diabetes Foundation (2015.82.1824) and the European Foundation for the Study of Diabetes (EFSD EFSD/AZ Macrovascular Programme 2015). N.P.R. received an IN-CONTROL CVON grant from the Dutch Heart Foundation (CVON2018-27) and a grant of the ERA-CVD Joint Transnational Call 2018, which is supported by the Dutch Heart Foundation (JTC2018, project MEMORY, 2018T093).

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. K.T. and X.A.M.H.v.D. wrote the first drafts of the manuscript. K.T., X.A.M.H.v.D., A.W.M.J., and J.P.B. performed the experiments and analyzed the data. K.T. and A.W.M.J. performed the study. K.T., A.W.M.J., N.P.R., C.J.T., and R.S. conceived and planned the study. All authors provided critical feedback and helped to shape the research and the manuscript. R.S. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

1.
Ratter
JM
,
Tack
CJ
,
Netea
MG
,
Stienstra
R
.
Environmental signals influencing myeloid cell metabolism and function in diabetes
.
Trends Endocrinol Metab
2018
;
29
:
468
480
2.
Lachmandas
E
,
Thiem
K
,
van den Heuvel
C
, et al
.
Patients with type 1 diabetes mellitus have impaired IL-1β production in response to Mycobacterium tuberculosis
.
Eur J Clin Microbiol Infect Dis
2018
;
37
:
371
380
3.
Pearson-Stuttard
J
,
Blundell
S
,
Harris
T
,
Cook
DG
,
Critchley
J
.
Diabetes and infection: assessing the association with glycaemic control in population-based studies
.
Lancet Diabetes Endocrinol
2016
;
4
:
148
158
4.
Flynn
MC
,
Kraakman
MJ
,
Tikellis
C
, et al
.
Transient intermittent hyperglycemia accelerates atherosclerosis by promoting myelopoiesis
.
Circ Res
2020
;
127
:
877
892
5.
Flynn
MC
,
Pernes
G
,
Lee
MKS
,
Nagareddy
PR
,
Murphy
AJ
.
Monocytes, macrophages, and metabolic disease in atherosclerosis
.
Front Pharmacol
2019
;
10
:
666
6.
Harcourt
BE
,
Penfold
SA
,
Forbes
JM
.
Coming full circle in diabetes mellitus: from complications to initiation
.
Nat Rev Endocrinol
2013
;
9
:
113
123
7.
Hainsworth
DP
,
Bebu
I
,
Aiello
LP
, et al.;
Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Research Group
.
Risk factors for retinopathy in type 1 diabetes: the DCCT/EDIC study
.
Diabetes Care
2019
;
42
:
875
882
8.
Carey
IM
,
Critchley
JA
,
DeWilde
S
,
Harris
T
,
Hosking
FJ
,
Cook
DG
.
Risk of infection in type 1 and type 2 diabetes compared with the general population: a matched cohort study
.
Diabetes Care
2018
;
41
:
513
521
9.
Critchley
JA
,
Carey
IM
,
Harris
T
,
DeWilde
S
,
Hosking
FJ
,
Cook
DG
.
Glycemic control and risk of infections among people with type 1 or type 2 diabetes in a large primary care cohort study
.
Diabetes Care
2018
;
41
:
2127
2135
10.
Corrales
JJ
,
Almeida
M
,
Burgo
RM
,
Hernández
P
,
Miralles
JM
,
Orfao
A
.
Decreased production of inflammatory cytokines by circulating monocytes and dendritic cells in type 2 diabetic men with atherosclerotic complications
.
J Diabetes Complications
2007
;
21
:
41
49
11.
Gacka
M
,
Dobosz
T
,
Szymaniec
S
,
Bednarska-Chabowska
D
,
Adamiec
R
,
Sadakierska-Chudy
A
.
Proinflammatory and atherogenic activity of monocytes in type 2 diabetes
.
J Diabetes Complications
2010
;
24
:
1
8
12.
Giulietti
A
,
van Etten
E
,
Overbergh
L
,
Stoffels
K
,
Bouillon
R
,
Mathieu
C
.
Monocytes from type 2 diabetic patients have a pro-inflammatory profile. 1,25-Dihydroxyvitamin D3 works as anti-inflammatory
.
Diabetes Res Clin Pract
2007
;
77
:
47
57
13.
Marhoffer
W
,
Stein
M
,
Maeser
E
,
Federlin
K
.
Impairment of polymorphonuclear leukocyte function and metabolic control of diabetes
.
Diabetes Care
1992
;
15
:
256
260
14.
Jha
AK
,
Huang
SC
,
Sergushichev
A
, et al
.
Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization
.
Immunity
2015
;
42
:
419
430
15.
Lachmandas
E
,
Boutens
L
,
Ratter
JM
, et al
.
Microbial stimulation of different Toll-like receptor signalling pathways induces diverse metabolic programmes in human monocytes
.
Nat Microbiol
2016
;
2
:
16246
16.
Freemerman
AJ
,
Johnson
AR
,
Sacks
GN
, et al
.
Metabolic reprogramming of macrophages: glucose transporter 1 (GLUT1)-mediated glucose metabolism drives a proinflammatory phenotype
.
J Biol Chem
2014
;
289
:
7884
7896
17.
Barry
JC
,
Shakibakho
S
,
Durrer
C
, et al
.
Hyporesponsiveness to the anti-inflammatory action of interleukin-10 in type 2 diabetes
.
Sci Rep
2016
;
6
:
21244
18.
Lin
K
,
Kools
H
,
de Groot
PJ
, et al
.
MADMAX - Management and analysis database for multiple ∼omics experiments
.
J Integr Bioinform
2011
;
8
:
160
19.
Irizarry
RA
,
Hobbs
B
,
Collin
F
, et al
.
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
.
Biostatistics
2003
;
4
:
249
264
20.
Carvalho
BS
,
Irizarry
RA
.
A framework for oligonucleotide microarray preprocessing
.
Bioinformatics
2010
;
26
:
2363
2367
21.
Dai
M
,
Wang
P
,
Boyd
AD
, et al
.
Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data
.
Nucleic Acids Res
2005
;
33
:
e175
22.
Ritchie
ME
,
Phipson
B
,
Wu
D
, et al
.
limma powers differential expression analyses for RNA-sequencing and microarray studies
.
Nucleic Acids Res
2015
;
43
:
e47
23.
Sartor
MA
,
Tomlinson
CR
,
Wesselkamper
SC
,
Sivaganesan
S
,
Leikauf
GD
,
Medvedovic
M
.
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments
.
BMC Bioinformatics
2006
;
7
:
538
24.
Ritchie
ME
,
Diyagama
D
,
Neilson
J
, et al
.
Empirical array quality weights in the analysis of microarray data
.
BMC Bioinformatics
2006
;
7
:
261
25.
Liu
R
,
Holik
AZ
,
Su
S
, et al
.
Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses
.
Nucleic Acids Res
2015
;
43
:
e97
26.
Benjamini
Y
,
Hochberg
Y
.
Controlling the false discovery rate - a practical and powerful approach to multiple testing
.
J R Stat Soc Series B Stat Methodol
1995
;
57
:
289
300
27.
Johnson
GL
,
Nakamura
K
.
The c-jun kinase/stress-activated pathway: regulation, function and role in human disease
.
Biochim Biophys Acta
2007
;
1773
:
1341
1348
28.
Schonthaler
HB
,
Guinea-Viniegra
J
,
Wagner
EF
.
Targeting inflammation by modulating the Jun/AP-1 pathway
.
Ann Rheum Dis
2011
;
70
(
Suppl. 1
):
i109
i112
29.
Mooradian
AD
,
Reed
RL
,
Meredith
KE
,
Scuderi
P
.
Serum levels of tumor necrosis factor and IL-1 alpha and IL-1 beta in diabetic patients
.
Diabetes Care
1991
;
14
:
63
65
30.
Foss-Freitas
MC
,
Foss
NT
,
Rassi
DM
,
Donadi
EA
,
Foss
MC
.
Evaluation of cytokine production from peripheral blood mononuclear cells of type 1 diabetic patients
.
Ann N Y Acad Sci
2008
;
1150
:
290
296
31.
Ohno
Y
,
Aoki
N
,
Nishimura
A
.
In vitro production of interleukin-1, interleukin-6, and tumor necrosis factor-alpha in insulin-dependent diabetes mellitus
.
J Clin Endocrinol Metab
1993
;
77
:
1072
1077
32.
Devaraj
S
,
Glaser
N
,
Griffen
S
,
Wang-Polagruto
J
,
Miguelino
E
,
Jialal
I
.
Increased monocytic activity and biomarkers of inflammation in patients with type 1 diabetes
.
Diabetes
2006
;
55
:
774
779
33.
Zerr
KJ
,
Furnary
AP
,
Grunkemeier
GL
,
Bookin
S
,
Kanhere
V
,
Starr
A
.
Glucose control lowers the risk of wound infection in diabetics after open heart operations
.
Ann Thorac Surg
1997
;
63
:
356
361
34.
Nathan
DM
,
Genuth
S
,
Lachin
J
, et al.;
Diabetes Control and Complications Trial Research Group
.
The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus
.
N Engl J Med
1993
;
329
:
977
986
35.
Nathan
DM
,
Cleary
PA
,
Backlund
JY
, et al.;
Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study Research Group
.
Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes
.
N Engl J Med
2005
;
353
:
2643
2653
36.
Meza
CA
,
La Favor
JD
,
Kim
DH
,
Hickner
RC
.
Endothelial dysfunction: is there a hyperglycemia-induced imbalance of NOX and NOS
?
Int J Mol Sci
2019
;
20
:
3775
37.
Oyama
T
,
Miyasita
Y
,
Watanabe
H
,
Shirai
K
.
The role of polyol pathway in high glucose-induced endothelial cell damages
.
Diabetes Res Clin Pract
2006
;
73
:
227
234
38.
Chung
SS
,
Ho
EC
,
Lam
KS
,
Chung
SK
.
Contribution of polyol pathway to diabetes-induced oxidative stress
.
J Am Soc Nephrol
2003
;
14
(
Suppl. 3
):
S233
S236
39.
Yamagishi
S
,
Maeda
S
,
Matsui
T
,
Ueda
S
,
Fukami
K
,
Okuda
S
.
Role of advanced glycation end products (AGEs) and oxidative stress in vascular complications in diabetes
.
Biochim Biophys Acta
2012
;
1820
:
663
671
40.
Kitahara
M
,
Eyre
HJ
,
Lynch
RE
,
Rallison
ML
,
Hill
HR
.
Metabolic activity of diabetic monocytes
.
Diabetes
1980
;
29
:
251
256
41.
Kelly
B
,
O’Neill
LA
.
Metabolic reprogramming in macrophages and dendritic cells in innate immunity
.
Cell Res
2015
;
25
:
771
784
42.
O’Neill
LA
,
Pearce
EJ
.
Immunometabolism governs dendritic cell and macrophage function
.
J Exp Med
2016
;
213
:
15
23
43.
Guha
M
,
Mackman
N
.
LPS induction of gene expression in human monocytes
.
Cell Signal
2001
;
13
:
85
94
44.
Biswas
SK
,
Lopez-Collazo
E
.
Endotoxin tolerance: new mechanisms, molecules and clinical significance
.
Trends Immunol
2009
;
30
:
475
487
45.
Li
L
,
Pan
Z
,
Yang
X
.
Identification of dynamic molecular networks in peripheral blood mononuclear cells in type 1 diabetes mellitus
.
Diabetes Metab Syndr Obes
2019
;
12
:
969
982
46.
Honkanen
J
,
Nieminen
JK
,
Gao
R
, et al
.
IL-17 immunity in human type 1 diabetes
.
J Immunol
2010
;
185
:
1959
1967
47.
Katz
S
,
Klein
B
,
Elian
I
,
Fishman
P
,
Djaldetti
M
.
Phagocytotic activity of monocytes from diabetic patients
.
Diabetes Care
1983
;
6
:
479
482
48.
Setiadi
H
,
Wautier
JL
,
Courillon-Mallet
A
,
Passa
P
,
Caen
J
.
Increased adhesion to fibronectin and Mo-1 expression by diabetic monocytes
.
J Immunol
1987
;
138
:
3230
3234
49.
Kunt
T
,
Forst
T
,
Früh
B
, et al
.
Binding of monocytes from normolipidemic hyperglycemic patients with type 1 diabetes to endothelial cells is increased in vitro
.
Exp Clin Endocrinol Diabetes
1999
;
107
:
252
256
50.
Sen
P
,
Dickens
AM
,
López-Bascón
MA
, et al
.
Metabolic alterations in immune cells associate with progression to type 1 diabetes
.
Diabetologia
2020
;
63
:
1017
1031
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.