OBJECTIVE—There is evidence that monocytes of patients with type 1 diabetes show proinflammatory activation and disturbed migration/adhesion, but the evidence is inconsistent. Our hypothesis is that monocytes are distinctly activated/disturbed in different subforms of autoimmune diabetes.

RESEARCH DESIGN AND METHODS—We studied patterns of inflammatory gene expression in monocytes of patients with type 1 diabetes (juvenile onset, n = 30; adult onset, n = 30) and latent autoimmune diabetes of the adult (LADA) (n = 30) (controls subjects, n = 49; type 2 diabetic patients, n = 30) using quantitative PCR. We tested 25 selected genes: 12 genes detected in a prestudy via whole-genome analyses plus an additional 13 genes identified as part of a monocyte inflammatory signature previously reported.

RESULTS—We identified two distinct monocyte gene expression clusters in autoimmune diabetes. One cluster (comprising 12 proinflammatory cytokine/compound genes with a putative key gene PDE4B) was detected in 60% of LADA and 28% of adult-onset type 1 diabetic patients but in only 10% of juvenile-onset type 1 diabetic patients. A second cluster (comprising 10 chemotaxis, adhesion, motility, and metabolism genes) was detected in 43% of juvenile-onset type 1 diabetic and 33% of LADA patients but in only 9% of adult-onset type 1 diabetic patients.

CONCLUSIONS—Subgroups of type 1 diabetic patients show an abnormal monocyte gene expression with two profiles, supporting a concept of heterogeneity in the pathogenesis of autoimmune diabetes only partly overlapping with the presently known diagnostic categories.

There is evidence that monocytes of patients with type 1 diabetes are functionally aberrant, showing raised production of interleukin (IL)-1β, IL-6, superoxide anion, and prostaglandin-endoperoxide synthase 2 (PTGS2) (1–3); aberrant generation of antigen-presenting cells (4,5); and abnormal chemotaxis, adhesion, and migratory potential (6). These aberrancies are thought to play a role in the pathogenesis of the disease by disrupting tolerance and aggravating the β-cell cytotoxic potential of infiltrating monocyte-derived dendritic cells and macrophages. However, these aberrant functional findings could not always be reproduced, particularly with regard to the enhanced production of PTGS2 (7) and the poor generation of antigen-presenting cells from monocytes (8). Two issues could be relevant to these discrepancies. First, raised production of proinflammatory monocyte-derived cytokines could be related to hyperglycemia (9). Second, there might be heterogeneity within autoimmune diabetes, such as has been noted previously between adult and juvenile forms of type 1 diabetes on the basis of genetic, immune, and metabolic characteristics (10). This possible heterogeneity in autoimmune diabetes might also become evident in different monocyte activation profiles.

To resolve these issues, we focus here on patterns of inflammatory gene expression in monocytes from selected patients distinguished by clinical characteristics and age at diagnosis, as well as from control subjects. Our hypothesis is that monocytes might be distinctly activated and disturbed within the known diagnostic categories of diabetes.

Recently, we reported a signature of 18 inflammatory-related genes in monocytes of bipolar patients (11); activated monocytes are thought to play a role in the pathogenesis of bipolar disorder (12,13). Given the reported association between bipolar disorder and autoimmune diabetes (14), and given the possible central role of monocytes in both disorders, we tested this set of 18 proinflammatory monocyte genes in patients with autoimmune diabetes. To these 18 monocyte genes, we added 7 genes identified in a whole-genome expression profile of a set of juvenile-onset type 1 diabetic patients who had been compared with healthy control subjects and type 2 diabetic patients (see supplementary Fig. 1 [available in an online appendix at http://dx.doi.org/10.2337/db08-0496]). Thus, using quantitative RT-PCR (Q-PCR), we validated abnormal expression of 25 monocyte activation genes in latent autoimmune diabetes of the adult (LADA), adult-onset type 1 diabetic and juvenile-onset type 1 diabetic patients, and, as controls, type 2 diabetic patients and healthy subjects.

All participants were diagnosed with diabetes according to the criteria of the American Diabetes Association (15). The characteristics of both patients and control subjects are shown in a supplementary Table A.

The methods of blood collection and storage, preparation of purified CD14+ monocytes, mRNA isolation, and Q-PCR have been described in detail elsewhere (11) and are given in the legend of supplementary Table B. Statistics used have been given in the legends of the various tables and figures.

Q-PCR analysis.

Table 1 shows the gene expression levels of 24 of 25 selected genes in the monocytes of the tested diabetic groups. These 25 genes comprised 12 genes (10 upregulated and 2 downregulated), identified in a prestudy (Affymetrix gene expression profiling, supplementary Fig. 1), that differentiated type 1 diabetes monocytes from both type 2 diabetes and control monocytes (fourfold difference, P < 0.01). These 12 genes were STX1A, DHRS3 (SDR1), FABP5, CD9, CDC42, chemokine ligand 2 (CCL2), CCL7 (MCP-3), PTPN7, NAB2, and EMP1 (all upregulated) and BAZ1A and HSPA1A (each downregulated). The other 13 genes we tested (PDE4B, IL1B, IL6, tumor necrosis factor (TNF), PTGS2, pentraxin 3 (PTX3), CCL20, CXCL2, MAPK6, DUSP2, ATF3, TNFAIP3, and BCL2A1) were reported elsewhere (11) as a coherent and mutually correlating set (signature) of 18 aberrantly expressed inflammation-related genes in monocytes of bipolar patients; 5 of the 18 genes, i.e., CDC42, CCL2, CCL7, NAB2, and EMP1, were also detected in our Affymetrix prestudy in purified type 1 diabetes monocytes.

In Table 1, data are given as relative fold changes, a method that is widely used but that has potential limitations (e.g., less accurate for genes with a large difference from the reference gene; for raw cycle threshold values see supplementary Table C), as does our standardization of patient data to control subjects (which was done to correct for the observed interassay variation). To address the latter issue, data were also analyzed before standardization to control subjects (supplementary Table D). In essence, the same conclusions can be drawn from both analyses: 1) 24 of 25 studied genes were validated as aberrantly expressed (BAZ1A was not abnormally expressed); 2) although monocytes of juvenile-onset type 1 diabetic, adult-onset type 1 diabetic, LADA, and type 2 diabetic patients all showed enhanced gene expression of many of the inflammatory genes compared with control subjects, they also showed differences compared with each other; and 3) some of the genes were specific for a diagnostic category. The upregulation of PDE4B, TNFAIP3, and MAPK6 were specific for LADA monocytes; the upregulation of FABP5 and the downregulation of HSPA1A were specific for juvenile-onset type 1 diabetes monocytes. Neither adult-onset type 1 diabetes nor type 2 diabetes had an up- or downregulation of a specific gene, although type 2 diabetes monocytes showed a clear upregulation of many of the inflammatory genes.

The gene expression levels within each subject group did not correlate with A1C, BMI, age, sex, age at onset of diabetes, or disease duration (tested by ANCOVA). To further analyze the data, we embarked on cluster analysis.

Identification of two gene expression clusters and their presence in LADA, type 1 diabetic, and type 2 diabetic patients.

Figure 1 shows the Q-PCR data of the patients and control subjects in hierarchical cluster analysis. The dendrogram of average linkage showed two interdependent main gene clusters.

In another and different cluster analysis of the Q-PCR data, we correlated the expression levels of the 24 abnormally expressed genes to the expression level of the following: 1) PDE4B, because it is one of the genes specific for LADA and a putative key gene for cluster 1 (see discussion), and 2) FABP5, because this cluster 2 gene is specific for juvenile-onset type 1 diabetes. Table 2 shows that around these specific genes, two mutually correlating gene expression sets appeared. In the PDE4B-correlating set, all cluster 1 and cluster 2 genes (apart from FABP5) were present and correlated strongly with the gene expression of PDE4B. In the FABP5-correlating set, almost all cluster 2 genes were present (except for NAB2), along with CXCL2, PTGS2, HSPA1A, and CD9. Interestingly, PTGS2 was overexpressed in the PDE4B-positive subjects, whereas its expression was reduced in FABP5-positive subjects (Table 2).

We next sought the relationship of different patient groups to cluster 1 and cluster 2 genes (Table 3). Cluster 1 and the PDE4B-correlating set were significantly more frequent in adult-onset type 1 diabetic, LADA, and type 2 diabetic patients compared with control subjects, whereas control subjects and juvenile-onset type 1 diabetic patients were similar in this regard. Cluster 2 and the FABP5-correlating set were significantly more frequent in both juvenile-onset type 1 diabetic and LADA patients compared with control subjects, adult-onset type 1 diabetic, and type 2 diabetic patients. Neither clusters nor specific PDE4B or FABP5 gene expression were related to age, A1C, glucose level, or BMI within any of the groups studied.

Correlations between gene expression levels in circulating monocytes and serum levels of cytokines.

In addition to monocyte gene analysis, we determined serum levels of IL-6, tumor necrosis factor–α, pentraxin 3 (PTX3), and CCL2 in patients and control subjects (for data see supplementary Fig. 2) and correlated gene expression levels to corresponding serum cytokine levels. Monocyte gene expression levels of PTX3 and IL6 (PTX3: r = 0.26, P = 0.004; IL-6: r = 0.23, P = 0.034; Spearman's correlation), but not of TNF and CCL2, correlated with serum protein levels. A possible explanation for this observed discrepancy between mRNA and protein expression levels is that serum levels of cytokines are more subject to confounders (e.g., BMI, glucose levels) than gene expression levels, as is suggested by our data (supplementary Fig. 2).

We also compared the serum cytokine levels of cluster-positive and cluster-negative patients. We found higher serum levels of PTX3 in cluster 1–positive compared with cluster 1–negative patients (Fig. 2), suggesting an in vivo relevance of at least cluster 1 gene expression. Elevated levels of serum PTX3, a novel acute phase protein, have been found in other autoimmune conditions such as rheumatoid arthritis and scleroderma (16).

This study shows two distinct monocyte gene-expression profiles in autoimmune diabetes, indicating different activation profiles, which suggests heterogeneity in the pathogenesis of autoimmune diabetes.

We identified one profile of mainly proinflammatory genes (IL1B, IL6, TNF, PTGS2, PTX3, CCL20, CXCL2, DUSP2, ATF3, TNFAIP3, and BCL2A1) with a putative key gene PDE4B. PDE4B is a c-AMP–degrading enzyme and could be a key molecule for turning monocytes into high proinflammatory cytokine–producing cells, as targeted gene knockout studies show that phosphodiesterase 4B (PDE4B) has a crucial role in the cytokine production of monocytes (17–20). A second profile consisted of genes mainly involved in chemotaxis, adhesion, motility, and metabolism (CCL7, CCL2, CDC42, STX1A, EMP1, FABP5, DHRS3, NAB2, PTPN7, and MAPK6), with a putative key gene FABP5.

The first profile (cluster 1) was found in monocytes in LADA (60%) and adult-onset type 1 diabetic patients (28%) more than in juvenile-onset type 1 diabetic patients and control subjects (each 10%). The second profile (cluster 2), conversely, was found in 43% of juvenile-onset type 1 diabetic and in 33% of LADA patients but in <10% each of adult-onset type 1 diabetic patients and control subjects. These different frequencies of the two activation clusters in the known diagnostic categories of diabetes are consistent with the view that the categories are pathologically different, such that LADA and adult-onset type 1 diabetes have similar immune characteristics distinct from juvenile-onset type 1 diabetes (10).

We also found many of the inflammatory genes upregulated in type 2 diabetes monocytes, supporting the view that inflammatory monocytes are involved in the pathogenesis of type 2 diabetes (21). However, most (83–100%) type 2 diabetic patients had normal expression of the key genes PDE4B and FABP5, which resulted in their monocyte gene cluster being distinct from that in the majority of LADA and juvenile-onset type 1 diabetic patients.

Because the monocytes appear to be distinctly activated and disturbed in LADA, adult-onset type 1 diabetes, juvenile-onset type 1 diabetes, and type 2 diabetes, it is possible that these profiles can be used to identify subforms of diabetes within the known diagnostic categories of diabetes. This subdivision could improve outcome prediction and gene-association studies, may lead to more consistent reports on immune aberrancies in autoimmune diabetes, and could result in new intervention strategies by providing new targets for treatment. PDE4B, in particular, might be such a target, as inhibitors are in development (17–20), and rolipram, an archetypical PDE4 inhibitor, reduced insulitis and prevented diabetes in the nonobese diabetic (NOD) mouse (22). Another potential target for drug intervention is PTGS2, a key enzyme in the biosynthesis of prostanoids. COX-2 (PTGS2) inhibitors are well known for their anti-inflammatory functions (23), but there are no studies of them in diabetic patients. Of note, both a raised and normal basal PTGS2 have been described in type 1 diabetes (3,7); here we find that PTGS2 is raised in cluster 1 but downregulated in cluster 2–positive type 1 diabetic patients. Thus, COX-2 inhibitors might alter monocyte activation in cluster 1–positive patients (i.e., many LADA and adult-onset type 1 diabetic patients) but not in cluster 2–positive patients (i.e., many juvenile-onset type 1 diabetic patients).

A recent study (24) showed that factors in serum of type 1 diabetic patients could induce inflammatory genes (CCL2, CCL7, IL1B) in peripheral blood mononuclear cells (PBMCs). Another gene expression study of PBMCs of type 1 diabetic patients (25) also detected overexpression of inflammatory genes (among others, IL1B and PTGS2), without evidence of the extended signatures described here. In this latter study, investigators used nonfractionated PBMCs. Indeed, an important issue is the cell collection, preservation, and separation used in our study. We used frozen-stored PBMCs and positive CD14 magnetic cell sorting separation. Specifically, freeze-storing might induce differences in gene expression, whereas positive magnetic cell sorting separation does not influence gene expression (11,26). Alternative monocyte separation techniques do modify gene expression profiles, e.g., we found plastic adhered monocytes to downregulate PDE4B gene expression (as is known when monocytes change into macrophages [27]). However, despite these limitations, the differences we describe cannot be due to freeze storage because all monocytes are handled similarly. Further investigations are needed to establish consistency and diagnostic and prognostic consequences of monocyte inflammatory profiles under various storage and isolation conditions.

The LADA Consortium: R.D. Leslie, M. Hawa, P. Pozzilli, R. Williams, S. Brophy, H. Davies, H. Beck-Nielsen, K. Yderstraede, D. Hadden, S. Hunter, R. Buzzetti, W. Scherbaum, J. Siessler, N.C. Schloot, H. Kolb, G. Schernthaner, J. Tuomilehto, C. Sarti, A. De Leiva, D. Mauricio, E. Brugues, C. Thivolet.

FIG. 1.
FIG. 1. Color-coded correlation matrix illustrating pairwise correlations between the expression levels of the 24 genes aberrantly expressed in patients with various forms of diabetes (Table 1). Blue squares indicate negative correlations; red squares indicate positive correlations. The color intensities code for the strength of the correlations. Also, a dendrogram is presented as a result of hierarchical cluster analysis with the use of correlation coefficients. The dendrogram shows two gene-expression clusters.
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Color-coded correlation matrix illustrating pairwise correlations between the expression levels of the 24 genes aberrantly expressed in patients with various forms of diabetes (Table 1). Blue squares indicate negative correlations; red squares indicate positive correlations. The color intensities code for the strength of the correlations. Also, a dendrogram is presented as a result of hierarchical cluster analysis with the use of correlation coefficients. The dendrogram shows two gene-expression clusters.

FIG. 1.
FIG. 1. Color-coded correlation matrix illustrating pairwise correlations between the expression levels of the 24 genes aberrantly expressed in patients with various forms of diabetes (Table 1). Blue squares indicate negative correlations; red squares indicate positive correlations. The color intensities code for the strength of the correlations. Also, a dendrogram is presented as a result of hierarchical cluster analysis with the use of correlation coefficients. The dendrogram shows two gene-expression clusters.
View largeDownload slide

Color-coded correlation matrix illustrating pairwise correlations between the expression levels of the 24 genes aberrantly expressed in patients with various forms of diabetes (Table 1). Blue squares indicate negative correlations; red squares indicate positive correlations. The color intensities code for the strength of the correlations. Also, a dendrogram is presented as a result of hierarchical cluster analysis with the use of correlation coefficients. The dendrogram shows two gene-expression clusters.

Close modal
FIG. 2.
FIG. 2. Serum levels of PTX3 in cluster 1–positive (n = 36) and –negative subjects (n = 73) (patients as well as control subjects). The definition was as follows: positive, ≥75% of the cluster 1 genes positive; negative, <75% of the cluster 1 genes positive. Groups were compared by ANCOVA analysis with age, sex, and BMI included in the model. Because normal distribution of PTX3 could not be obtained, ranks of PTX3 were used in the analysis (28).
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Serum levels of PTX3 in cluster 1–positive (n = 36) and –negative subjects (n = 73) (patients as well as control subjects). The definition was as follows: positive, ≥75% of the cluster 1 genes positive; negative, <75% of the cluster 1 genes positive. Groups were compared by ANCOVA analysis with age, sex, and BMI included in the model. Because normal distribution of PTX3 could not be obtained, ranks of PTX3 were used in the analysis (28).

FIG. 2.
FIG. 2. Serum levels of PTX3 in cluster 1–positive (n = 36) and –negative subjects (n = 73) (patients as well as control subjects). The definition was as follows: positive, ≥75% of the cluster 1 genes positive; negative, <75% of the cluster 1 genes positive. Groups were compared by ANCOVA analysis with age, sex, and BMI included in the model. Because normal distribution of PTX3 could not be obtained, ranks of PTX3 were used in the analysis (28).
View largeDownload slide

Serum levels of PTX3 in cluster 1–positive (n = 36) and –negative subjects (n = 73) (patients as well as control subjects). The definition was as follows: positive, ≥75% of the cluster 1 genes positive; negative, <75% of the cluster 1 genes positive. Groups were compared by ANCOVA analysis with age, sex, and BMI included in the model. Because normal distribution of PTX3 could not be obtained, ranks of PTX3 were used in the analysis (28).

Close modal
TABLE 1

Q-PCR analysis of monocytes of patients with various forms of diabetes compared with healthy control subjects

Juvenile-onset type 1 diabetes vs. control subjects*
Adult-onset type 1 diabetes vs. control subjects*
LADA vs. control subjects†
Type 2 diabetic vs. control subjects*
Fold change (95% CI)†PFold change (95% CI)†PFold change (95% CI)†PFold change (95% CI)†P
n 30  43  30  30  
Inflammation         
    PDE4B 0.71 (0.45–1.13) 0.313 1.26 (0.91–1.74) 0.360 2.86 (1.57–5.19) <0.001 1.32 (0.93–1.87) 0.358 
    IL6 0.83 (0.28–2.48) 0.977 3.05 (1.34–6.91) 0.013 17.65 (7.42–41.96) <0.001 6.82 (3.31–14.06) <0.001 
    Il1B 0.51 (0.25–1.04) 0.262 2.46 (1.34–4.51) 0.015 10.12 (4.37–23.47) <0.001 4.66 (2.68–8.10) <0.001 
    PTX3 0.95 (0.61–1.48) 0.770 1.79 (1.13–2.84) 0.127 4.69 (2.39–9.19) <0.001 2.23 (1.59–3.32) 0.003 
    PTGS2 0.32 (0.18–0.59) 0.011 2.16 (1.31–3.57) 0.006 8.09 (4.30–15.25) <0.001 3.98 (2.52–6.28) <0.001 
    TNF 0.57 (0.32–1.03) 0.216 1.20 (0.68–2.13) 0.627 6.32 (2.98–13.39) <0.001 2.95 (1.79–4.87) 0.005 
    TNFAIP3 0.83 (0.52–1.30) 0.619 1.24 (0.81–1.92) 0.502 4.05 (2.34–7.00) <0.001 1.71 (1.10–2.66) 0.107 
    HSPA1A 0.60 (0.41–0.88) 0.008 0.84 (0.71–1.00) 0.358 0.95 (0.56–1.59) 0.867 0.83 (0.61–1.13) 0.417 
Chemokinesis/motility/adhesion         
    CCL7 22.30 (6.61–75.17) <0.001 6.36 (2.37–17.05) <0.001 22.16 (6.87–71.48) <0.001 3.63 (1.30–10.18) 0.018 
    CCL20 0.80 (0.26–2.48) 0.953 3.00 (1.14–7.87) 0.054 33.17 (9.21–119.44) <0.001 7.80 (2.94–20.65) 0.002 
    CXCL2 1.50 (0.77–2.90) 0.270 2.21 (1.20–4.09) 0.028 9.48 (4.00–22.48) <0.001 4.92 (2.64–9.16) <0.001 
    CCL2 4.19 (1.82–9.63) 0.007 2.65 (1.55–4.54) 0.001 4.62 (2.14–9.97) <0.001 2.53 (1.37–4.67) 0.002 
    CDC42 1.98 (1.44–2.73) 0.005 1.29 (0.95–1.73) 0.119 2.03 (1.34–3.08) <0.001 1.44 (1.13–1.84) 0.041 
    CD9 2.13 (1.32–3.44) 0.116 1.40 (0.98–2.01) 0.047 2.04 (1.28–3.25) <0.001 2.13 (1.40–3.23) 0.028 
    STX1A 7.31 (3.61–14.83) <0.001 1.48 (0.98–2.24) 0.071 2.89 (1.68–4.97) <0.001 1.72 (1.06–2.77) 0.023 
Cell survival/apoptosis         
    BCL2A1 1.39 (0.89–2.17) 0.291 1.42 (1.01–1.99) 0.077 3.17 (1.87–5.37) <0.001 1.95 (1.40–2.72) 0.005 
    EMP1 2.47 (1.49–4.10) 0.008 1.29 (0.90–1.86) 0.180 3.49 (2.20–5.54) <0.001 2.13 (1.40–3.23) 0.002 
Mapk pathway         
    PTPN7 2.52 (1.83–3.49) <0.001 1.42 (1.07–1.87) 0.038 2.91 (1.82–4.64) <0.001 1.94 (1.42–2.64) 0.001 
    DUSP2 1.04 (0.69–1.58) 0.898 2.26 (1.31–3.90) 0.005 7.98 (4.24–15.02) <0.001 3.43 (2.00–5.88) <0.001 
    ATF3 0.88 (0.60–1.29) 0.531 2.02 (1.38–2.96) 0.001 6.07 (3.60–10.25) <0.001 2.81 (1.98–3.99) <0.001 
    NAB2 2.38 (1.32–4.28) 0.025 1.23 (0.77–1.97) 0.366 2.37 (1.23–4.58) 0.006 1.81 (1.09–3.00) 0.043 
    MAPK6 1.53 (1.09–2.16) 0.102 1.07 (0.83–1.39) 0.624 1.82 (1.04–3.19) 0.005 1.16 (0.79–1.70) 0.394 
Metabolism         
    FABP5 2.03 (1.25–3.29) 0.019 0.83 (0.65–1.06) 0.504 1.36 (0.74–2.49) 0.158 0.92 (0.59–1.45) 0.934 
    DHRS3 3.52 (1.60–7.72) 0.001 1.02 (0.75–1.37) 0.694 2.26 (1.43–3.90) 0.001 1.62 (1.08–2.42) 0.031 
Juvenile-onset type 1 diabetes vs. control subjects*
Adult-onset type 1 diabetes vs. control subjects*
LADA vs. control subjects†
Type 2 diabetic vs. control subjects*
Fold change (95% CI)†PFold change (95% CI)†PFold change (95% CI)†PFold change (95% CI)†P
n 30  43  30  30  
Inflammation         
    PDE4B 0.71 (0.45–1.13) 0.313 1.26 (0.91–1.74) 0.360 2.86 (1.57–5.19) <0.001 1.32 (0.93–1.87) 0.358 
    IL6 0.83 (0.28–2.48) 0.977 3.05 (1.34–6.91) 0.013 17.65 (7.42–41.96) <0.001 6.82 (3.31–14.06) <0.001 
    Il1B 0.51 (0.25–1.04) 0.262 2.46 (1.34–4.51) 0.015 10.12 (4.37–23.47) <0.001 4.66 (2.68–8.10) <0.001 
    PTX3 0.95 (0.61–1.48) 0.770 1.79 (1.13–2.84) 0.127 4.69 (2.39–9.19) <0.001 2.23 (1.59–3.32) 0.003 
    PTGS2 0.32 (0.18–0.59) 0.011 2.16 (1.31–3.57) 0.006 8.09 (4.30–15.25) <0.001 3.98 (2.52–6.28) <0.001 
    TNF 0.57 (0.32–1.03) 0.216 1.20 (0.68–2.13) 0.627 6.32 (2.98–13.39) <0.001 2.95 (1.79–4.87) 0.005 
    TNFAIP3 0.83 (0.52–1.30) 0.619 1.24 (0.81–1.92) 0.502 4.05 (2.34–7.00) <0.001 1.71 (1.10–2.66) 0.107 
    HSPA1A 0.60 (0.41–0.88) 0.008 0.84 (0.71–1.00) 0.358 0.95 (0.56–1.59) 0.867 0.83 (0.61–1.13) 0.417 
Chemokinesis/motility/adhesion         
    CCL7 22.30 (6.61–75.17) <0.001 6.36 (2.37–17.05) <0.001 22.16 (6.87–71.48) <0.001 3.63 (1.30–10.18) 0.018 
    CCL20 0.80 (0.26–2.48) 0.953 3.00 (1.14–7.87) 0.054 33.17 (9.21–119.44) <0.001 7.80 (2.94–20.65) 0.002 
    CXCL2 1.50 (0.77–2.90) 0.270 2.21 (1.20–4.09) 0.028 9.48 (4.00–22.48) <0.001 4.92 (2.64–9.16) <0.001 
    CCL2 4.19 (1.82–9.63) 0.007 2.65 (1.55–4.54) 0.001 4.62 (2.14–9.97) <0.001 2.53 (1.37–4.67) 0.002 
    CDC42 1.98 (1.44–2.73) 0.005 1.29 (0.95–1.73) 0.119 2.03 (1.34–3.08) <0.001 1.44 (1.13–1.84) 0.041 
    CD9 2.13 (1.32–3.44) 0.116 1.40 (0.98–2.01) 0.047 2.04 (1.28–3.25) <0.001 2.13 (1.40–3.23) 0.028 
    STX1A 7.31 (3.61–14.83) <0.001 1.48 (0.98–2.24) 0.071 2.89 (1.68–4.97) <0.001 1.72 (1.06–2.77) 0.023 
Cell survival/apoptosis         
    BCL2A1 1.39 (0.89–2.17) 0.291 1.42 (1.01–1.99) 0.077 3.17 (1.87–5.37) <0.001 1.95 (1.40–2.72) 0.005 
    EMP1 2.47 (1.49–4.10) 0.008 1.29 (0.90–1.86) 0.180 3.49 (2.20–5.54) <0.001 2.13 (1.40–3.23) 0.002 
Mapk pathway         
    PTPN7 2.52 (1.83–3.49) <0.001 1.42 (1.07–1.87) 0.038 2.91 (1.82–4.64) <0.001 1.94 (1.42–2.64) 0.001 
    DUSP2 1.04 (0.69–1.58) 0.898 2.26 (1.31–3.90) 0.005 7.98 (4.24–15.02) <0.001 3.43 (2.00–5.88) <0.001 
    ATF3 0.88 (0.60–1.29) 0.531 2.02 (1.38–2.96) 0.001 6.07 (3.60–10.25) <0.001 2.81 (1.98–3.99) <0.001 
    NAB2 2.38 (1.32–4.28) 0.025 1.23 (0.77–1.97) 0.366 2.37 (1.23–4.58) 0.006 1.81 (1.09–3.00) 0.043 
    MAPK6 1.53 (1.09–2.16) 0.102 1.07 (0.83–1.39) 0.624 1.82 (1.04–3.19) 0.005 1.16 (0.79–1.70) 0.394 
Metabolism         
    FABP5 2.03 (1.25–3.29) 0.019 0.83 (0.65–1.06) 0.504 1.36 (0.74–2.49) 0.158 0.92 (0.59–1.45) 0.934 
    DHRS3 3.52 (1.60–7.72) 0.001 1.02 (0.75–1.37) 0.694 2.26 (1.43–3.90) 0.001 1.62 (1.08–2.42) 0.031 
†

The quantitative value obtained from Q-PCR is a cycle threshold (CT). The fold change values between different groups were determined from normalized CT values (CT gene/CT reference gene ABL) by the ΔΔCT method (2−ΔΔCT, User Bulletin 2; Applied Biosystems, Foster City, CA). Data were standardized to the control subjects (thus, the control subjects were used as the calibrator). The fold change of the control subjects is therefore 1. The same data were also analyzed prior to standardization to the control subject group. These analyses are demonstrated in supplementary Table D.

*

Values >1, patients have a higher expression than control group; values <1, patients have a lower expression than control group; n = 59 control subjects. P tested by univariate ANCOVA vs. control subjects; age and sex are included in model.

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TABLE 2

Correlation of mRNAs

PDE4B correlating set
FABP5 correlating set
GenerPGenerP
PDE4B 1  PDE4B 0.011 0.96 
CCL20 0.88 <0.001 CCL20 0.20 0.39 
DUSP2 0.88 <0.001 DUSP2 0.14 0.45 
IL1B 0.87 <0.001 IL1B −0.043 0.96 
PTGS2 0.85 <0.001 PTGS2 −0.52 0.018 
IL6 0.84 <0.001 IL6 0.41 0.076 
BCL2A1 0.82 <0.001 BCL2A1 0.34 0.069 
PTX3 0.78 <0.001 PTX3 −0.13 0.58 
ATF3 0.76 <0.001 ATF3 0.21 0.26 
TNFAIP3 0.75 <0.001 TNFAIP3 0.081 0.67 
NAB2 0.69 <0.001 NAB2 0.23 0.22 
TNF 0.54 <0.001 TNF −0.25 0.19 
CXCL2 0.87 <0.001 CXCL2 0.60 <0.001 
CCL7 0.87 <0.001 CCL7 0.66 <0.001 
STX1A 0.79 <0.001 STX1A 0.68 <0.001 
CCL2 0.74 <0.001 CCL2 0.73 <0.001 
EMP1 0.67 <0.001 EMP1 0.57 0.001 
CDC42 0.63 <0.001 CDC42 0.48 0.008 
PTPN7 0.60 0.001 PTPN7 0.66 <0.001 
MAPK6 0.58 0.001 MAPK6 0.50 0.005 
DHRS3 0.58 0.001 DHRS3 0.66 <0.001 
CD9 −0.073 0.70 CD9 0.48 0.008 
HSPA1A −0.16 0.41 HSPA1A −0.36 0.005 
FABP5 0.27 0.15 FABP5 1  
PDE4B correlating set
FABP5 correlating set
GenerPGenerP
PDE4B 1  PDE4B 0.011 0.96 
CCL20 0.88 <0.001 CCL20 0.20 0.39 
DUSP2 0.88 <0.001 DUSP2 0.14 0.45 
IL1B 0.87 <0.001 IL1B −0.043 0.96 
PTGS2 0.85 <0.001 PTGS2 −0.52 0.018 
IL6 0.84 <0.001 IL6 0.41 0.076 
BCL2A1 0.82 <0.001 BCL2A1 0.34 0.069 
PTX3 0.78 <0.001 PTX3 −0.13 0.58 
ATF3 0.76 <0.001 ATF3 0.21 0.26 
TNFAIP3 0.75 <0.001 TNFAIP3 0.081 0.67 
NAB2 0.69 <0.001 NAB2 0.23 0.22 
TNF 0.54 <0.001 TNF −0.25 0.19 
CXCL2 0.87 <0.001 CXCL2 0.60 <0.001 
CCL7 0.87 <0.001 CCL7 0.66 <0.001 
STX1A 0.79 <0.001 STX1A 0.68 <0.001 
CCL2 0.74 <0.001 CCL2 0.73 <0.001 
EMP1 0.67 <0.001 EMP1 0.57 0.001 
CDC42 0.63 <0.001 CDC42 0.48 0.008 
PTPN7 0.60 0.001 PTPN7 0.66 <0.001 
MAPK6 0.58 0.001 MAPK6 0.50 0.005 
DHRS3 0.58 0.001 DHRS3 0.66 <0.001 
CD9 −0.073 0.70 CD9 0.48 0.008 
HSPA1A −0.16 0.41 HSPA1A −0.36 0.005 
FABP5 0.27 0.15 FABP5 1  

r is Spearman's correlation coefficient. The PDE4B correlations were determined in the LADA patients (n = 30) because PDE4B upregulation was specific for that group. The FABP5 correlations were determined in the juvenile-onset type 1 diabetic patients (n = 30) for the same reason (a specific FABP5 upregulation in juvenile-onset type 1 diabetes). See Fig. 1, the genes in the red box correlate significantly with PDE4B, and the genes in the blue box correlate significantly with FABP5.

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TABLE 3

The presence of cluster 1 and PDE4B–correlating set and cluster 2 and FABP5–correlating set in the monocytes of different diabetic groups and control subjects

DefinitionsControl subjectsJuvenile-onset type 1 diabetesAdult-onset type 1 diabetesLADAType 2 diabetes
n 94 30 43 30 30 
≥75% of cluster 1 genes positive 10 (9) 10 (3) 28 (12)* 60 (18)† 37 (11)‡ 
≥75% of PDE4B correlating set genes positive 3 (3) 10 (3) 23 (10)† 43 (13)† 17 (5)‡ 
≥75% of cluster 2 genes positive 5 (5) 43 (13)‡ 9 (4) 33 (10)‡ 10 (3) 
≥75% of FABP5 correlating set genes positive 2 (2) 43 (13)‡ 7 (3) 13 (4)* 0 
DefinitionsControl subjectsJuvenile-onset type 1 diabetesAdult-onset type 1 diabetesLADAType 2 diabetes
n 94 30 43 30 30 
≥75% of cluster 1 genes positive 10 (9) 10 (3) 28 (12)* 60 (18)† 37 (11)‡ 
≥75% of PDE4B correlating set genes positive 3 (3) 10 (3) 23 (10)† 43 (13)† 17 (5)‡ 
≥75% of cluster 2 genes positive 5 (5) 43 (13)‡ 9 (4) 33 (10)‡ 10 (3) 
≥75% of FABP5 correlating set genes positive 2 (2) 43 (13)‡ 7 (3) 13 (4)* 0 

Data are n (%) unless otherwise indicated. For this analysis, 35 extra control subjects were available, so 94 total were studied. Positivity of the genes is defined as an mRNA expression 1 SD higher than the mean level found in the control subjects.

*

P < 0.05;

†

P < 0.001;

‡

P < 0.01 vs. control subjects (tested via χ2 tests).

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Published ahead of print at http://diabetes.diabetesjournals.org on 3 July 2008.

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. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

These studies were supported by the European Union (EU) (MONODIAB, contract no. QLRT-1999-00276), the German Diabetes Association, and the Dutch Diabetic Foundation (contract no. 96.606); the Action LADA Consortium is also supported by the European Union (EU) (contract number no. QLG1-CT-2002-01886).

The authors thank Tar van Os for important contributions in preparing the figures and Caspar Looman for statistical advice. Also, we acknowledge Linda Bosch for her technical assistance regarding the Affymetrix experiments.

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DIABETES
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Supplementary data

Supplemental Tables and Figures- doc file