β-Cell function in women with L-T1D was recovered during gestation, but decreased after parturition
To assess the effect of pregnancy on β-cell function, fasting and stimulated C-peptide were measured. First, it was evaluated whether C-peptide becomes measurable during pregnancy in comparison with NP individuals. The frequency of women with L-T1D having detectable fasting C-peptide was consistently higher during gestation and after parturition (first trimester: 64.7%, third trimester: 76.5%, and 2 months post partum: 64.7%) than for NP women (26.7%, figure 1A). Despite an apparent variance within the NP group, the absolute concentration of fasting C-peptide was also higher in pregnant women than in NP individuals with L-T1D (figure 1B, table 1). This difference between the groups remained 2 months after parturition.
Fasting and glucose-induced C-peptide levels in pregnant women with diabetes. Peripheral venous blood was collected from pregnant individuals with long-standing type 1 diabetes (L-T1D, n=17) during three visits: first trimester (1T), third trimester (3T), and 2 months post partum (2PP). For comparison, plasma samples from non-pregnant women with L-T1D (NP, n=30) were used. A reduced oral glucose tolerance test (OGTT) was performed to assess the effect of pregnancy on glucose-induced insulin secretion. C-peptide in plasma samples obtained during fasting and the OGTT was measured by an ultrasensitive ELISA. (A) The number of individuals with non-detectable (ND) and detectable (D) fasting C-peptide was calculated. A χ2 test was applied to compare frequencies for both outcomes in each group. (B) The absolute concentration of fasting C-peptide was estimated between the NP group and pregnant women with L-T1D. Data were transformed to 10-logarithmic values. Dots and lines represent individual samples and median values, respectively. Two-sided Mann-Whitney U test was applied for statistical analyses between each visit and the NP group. Longitudinal changes in (C) absolute fasting C-peptide concentrations, (D) total C-peptide secretion, and (E) peak C-peptide concentrations during OGTT are shown in 10-logarithmic values. Differences between peak and fasting C-peptide values during OGTT are visualized as (F) absolute change and (G) percental change. Calculations were made on non-transformed data. Fasting C-peptide was set to 100% to calculate percental change during OGTT (dashed line). Graphs show median values and 95% CIs for each visit. Non-parametric Friedman test with uncorrected Dunn’s test for familywise α-threshold was applied for statistical analysis. *P<0.05, **p<0.01, ***p<0.001. AUC, area under the curve.
At a group level, fasting C-peptide levels did not change between gestational ages and the postpartum period (figure 1C). However, at the individual level most women with L-T1D had a rise in fasting C-peptide during pregnancy followed by a reduction or stabilization after parturition. Few participants had a nadir of fasting C-peptide that was recovered in the postpartum period. Both AUC and peak C-peptide after a glucose load increased during pregnancy (figure 1D,E). Moreover, the difference between the highest C-peptide concentrations and fasting values was enhanced between first and third trimesters (figure 1F). The glucose-induced insulin secretion declined after parturition, although the percental response during OGTT did not seem to be reduced (figure 1G). All C-peptide and glucose data from the OGTT is presented in online supplemental table 4.
The circulating protein levels of pregnant women with L-T1D is dynamic, but few functional clusters were related to the maternal immune system and potential modulators of β-cell function.
Plasma proteins associated with cellular and immunological processes were measured to investigate how they varies during gestation in women with T1D. Seventy-seven of 85 samples for Olink CELL REGULATION and 78 of 85 samples for Olink IMMUNE RESPONSE passed the quality control (online supplemental figure 1), rendering 16 samples from the first trimester, 15 samples from the third trimester, and 15 samples from the postpartum period for analysis. All 184 analytes were included in the analysis, as they were detected in ≥25% of the samples. First, a two-dimensional projection of the multivariate dataset and samples was computed by unsupervised PCA to identify outliers and potential clusters. Neither outliers nor clusters were found among the samples, meaning that the study groups had similar protein concentrations at a global level (online supplemental figure 2).
To investigate whether the protein composition diverges during pregnancy from a ‘T1D-background’, plasma samples from each trimester were compared with the NP individuals. Only 16 analytes were differently abundant between women during early gestation and NP participants with L-T1D, whereas the discrepancy between later gestational ages and the control cohort was enhanced (online supplemental table 3). A supervised network analysis was then performed to investigate functional associations between diverging analytes (n=49). A network comprising 11 edges between 16 nodes was identified, which could be divided into five clusters (PPI value=0.0008, figure 2A). Only 35% of the network was associated with maternal immune processes as visualized by functional enrichments. The largest cluster comprised several nodes that had a single interaction with the pleiotropic cytokine interleukin (IL)-6. Levels of IL-6 were higher in women with L-T1D during the third trimester than in the NP group (figure 2B). The chemokine CCL11 (eotaxin) was continuously less abundant in the pregnant women than in the NP individuals. Fibroblast growth factor (FGF)-21 was functionally connected to glucagon (GCG) signaling, and plasma levels of this factor were lower at first trimester compared with NP women with L-T1D. IL-6 was directly associated with amphiregulin (AREG), which is a protein that mediates PG production in placental tissue.20 21 AREG was also higher during the whole gestational period than in the NP group. Cytoskeleton-associated protein (CKAP)4 was associated with neutrophil-mediated immunity and was more abundant during pregnancy than in the NP participants. CKAP4 was connected to members of the C-type lectin (CLEC) domain family. CLEC4C was less abundant, while CLEC4D and CLEC7A were more abundant during the third trimester than in the NP women. A cluster classified as endocrine modification was identified, which comprised prokineticin (PROK)-1 and GCG (figure 2A and C). PROK-1 was more abundant throughout gestation in comparison with the NP participants, while plasma levels of GCG were only higher during the third trimester. A cluster related to antigen endocytosis was identified, where CLEC4A and lymphocyte antigen (LY)75 were reduced in plasma from women with L-T1D at third trimester (figure 2A and D). Two minor clusters related to diabetic complications (figure 2E) and neutrophil-associated hydrolases (figure 2F) were also found.
Network analysis of divergent proteins between pregnant and non-pregnant women with diabetes. Peripheral venous blood was collected from women with long-standing type 1 diabetes (L-T1D) at two visits during pregnancy: first trimester (1T, n=16) and third trimester (3T, n=15). For comparison, plasma samples from non-pregnant women with L-T1D (NP, n=30) were used. Proximity extension assay was employed to measure 184 analytes in plasma. A network analysis was performed to assess functional and physical interactions between 49 diverging proteins (16 proteins for 1T vs NP, 47 proteins for 3T vs NP). (A) Nodes corresponding to 49 proteins (abbreviated names) were formed, and 11 edges (black lines) were identified in the network. This network had a PPI enrichment p value <0.001 and an average local clustering coefficient=0.265. Edge thickness indicates the confidence level of each interaction: high (0.700) and maximum (0.900) scores. Filled nodes represent proteins with a known or predicted three-dimensional structure. Functional enrichments within the network were computed, where colors represent annotations that could describe nodes connected by edges. Connected nodes were classified based on enrichments in the network and protein database explorations: (B) interleukin (IL)-6 governed pathways, (C) endocrine modification, (D) antigen processing, (E) diabetic complications, (F) and enzyme activity. Protein levels in plasma are shown as Normalized Protein eXpression (NPX) that is an arbitrary unit expressed on the log2-scale. Analytes that were detected below their lower limits of detection are indicated with dashed lines ([beta-galactosidase [GLB1]: 1.6 NPX, ARSB: −0.009 NPX). Dots represent individual samples and filled lines indicate mean values for the groups. Two-sided unpaired Welch’s t-test with adjustment for multiple testing was applied for statistical analysis, *q<0.05, **q<0.01, ***q<0.001, and ****q<0.0001. AREG, amphiregulin; CKAP4, cytoskeleton-associated protein; CLEC, C-type lectin; GCG, glucagon; PROK, prokineticin.
A linear mixed model was also applied to assess variations in the protein composition of individuals with L-T1D during pregnancy and after parturition. Temporal differences were identified for 79 analytes, where the largest contrast was observed between the third trimester and 2 months post partum (online supplemental table 3). The supervised network analysis showed that only 19 proteins formed 15 interactions at high confidence level, but this network was non-random (PPI value=0.0007, figure 3A). Only 35% of all nodes were related to the immune system, as visualized by functional enrichments within the network. IL-6 was again connected to CCL11, FGF-21, AREG, and CKAP4, but to some extent with different functional enrichments. IL-6 increased from first to third trimester, as mirrored by the comparison with the NP group, and remained stable after parturition (figure 3B). The nadir of CCL11 during gestation disappeared 2 months after parturition and became similar to levels found in the NP women. FGF-21 was associated with growth factor activity and cell death inhibition in this network (figure 3A), which increased in plasma until the third trimester and then declined during the postpartum period (figure 3B). AREG and CKAP4 peaked at third trimester prior to a decline after parturition. In contrast to the previous network analysis, IL-6 was connected to the T cell co-receptor CD28 and indirectly to integrin proteins. Soluble CD28 was continuously reduced during pregnancy, but it increased again after parturition to higher levels than at early gestational ages. PROK-1 and GCG still formed a functional interaction within this network, although parathyroid hormone-related peptide receptor was included (figure 3A). PROK-1 levels were constantly high in pregnant women with L-T1D prior to a nadir 2 months post partum, while GCG peaked at third trimester prior to a decrease after parturition (figure 3C). CLEC4A, LY75, CD83, and lysosomal associated membrane protein (LAMP)3 were classified into one cluster, as they are functionally related to antigen-presenting cells (APC). In addition, these four proteins had similar longitudinal changes in women with L-T1D: suppressed during pregnancy before a rebound after childbirth (figure 3D). Three enzymes catalyzing protein glycosylation, indirectly regulating leukocyte migration and extravasation, were predominant in plasma samples from the third trimester (figure 3A and E).
Network analysis of divergent proteins in pregnant women with diabetes. Plasma samples were obtained from pregnant women with long-standing type 1 diabetes (L-T1D) during three visits: first trimester (1T, n=16), third trimester (3T, n=15), and 2 months post partum (2PP, n=15). Proximity extension assay was employed to measure 184 analytes in plasma. A network analysis was performed to assess functional and physical interactions between 79 diverging proteins. (A) Nodes corresponding to all 79 proteins (abbreviated names) were formed, and 15 edges (black lines) were identified in the network. This network had a PPI enrichment p value <0.001 and an average local clustering coefficient=0.194. Edge thickness indicates the confidence level of each interaction: high (0.700) and maximum (0.900) scores. Filled nodes represent proteins with a known or predicted three-dimensional structure. Functional enrichments within the network were computed, where colors represent annotations that could describe nodes connected by edges. Connected nodes were classified based on enrichments in the network and protein database explorations: (B) IL-6 governed pathways, (C) endocrine modification, (D) antigen presentation pathway, (E) and enzyme activity. Protein levels in plasma are shown as Normalized Protein eXpression (NPX) that is an arbitrary unit expressed on the log2-scale. Analytes that were detected below their lower limits of detection are indicated with dashed lines (CD28: 0.98 NPX, PTH1R: 1.3 NPX). Dots and lines represent individual samples and repeated measures at the specified visits, respectively. A linear mixed effects model with maximum likelihood approach was chosen for parameter estimation. Visits and individuals constituted fixed and random effects, respectively. An unstructured covariance matrix was assumed. False discovery rate by the Benjamini-Hochberg method was applied for correction of multiple testing in the model, *q<0.05, **q<0.01, ***q<0.001, and ****q<0.0001. AREG, amphiregulin; CKAP4, cytoskeleton-associated protein; CLEC, C-type lectin; GCG, glucagon; PROK, prokineticin.