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. 2011 Oct;7(10):e1002257.
doi: 10.1371/journal.pcbi.1002257. Epub 2011 Oct 27.

Metabolic regulation in progression to autoimmune diabetes

Marko Sysi-Aho  1 , Andrey Ermolov, Peddinti V Gopalacharyulu, Abhishek Tripathi, Tuulikki Seppänen-Laakso, Johanna Maukonen, Ismo Mattila, Suvi T Ruohonen, Laura Vähätalo, Laxman Yetukuri, Taina Härkönen, Erno Lindfors, Janne Nikkilä, Jorma Ilonen, Olli Simell, Maria Saarela, Mikael Knip, Samuel Kaski, Eriika Savontaus, Matej Orešič
Affiliations

Metabolic regulation in progression to autoimmune diabetes

Marko Sysi-Aho et al. PLoS Comput Biol. 2011 Oct.

Abstract

Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede β-cell autoimmunity in humans and can be used to identify those children who subsequently progress to type 1 diabetes. The mechanisms behind these disturbances are unknown. Here we show the specificity of the pre-autoimmune metabolic changes, as indicated by their conservation in a murine model of type 1 diabetes. We performed a study in non-obese prediabetic (NOD) mice which recapitulated the design of the human study and derived the metabolic states from longitudinal lipidomics data. We show that female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, normoglycemia, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum group. Together, the findings indicate that autoimmune diabetes is preceded by a state of increased metabolic demands on the islets resulting in elevated insulin secretion and suggest alternative metabolic related pathways as therapeutic targets to prevent diabetes.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Reverse-translational setting of the study.
Starting from clinical observations using metabolomics , then proceeding via modeling and metabolomics to an experimental model using a similar study design, then evolving further to tissue-specific studies. Such an approach aims to facilitate studies of early prodromal phases of disease pathogenesis.
Figure 2
Figure 2. Normoglycemic female NOD mice which later progress to diabetes have elevated glucose stimulated plasma insulin and diminished lipids at an early age.
(A) Incidence of diabetes in female (n = 26) and male (n = 44) NOD mice included in the longitudinal lipidomics study. The cumulative incidence of diabetes in this study was lower than the colony incidence of 80% in females and 35% in males. (B) Age-dependent progression of lipidomic profiles in females, viewed as ratios of mean lipid concentrations of diabetes progressors (n = 12) vs. non-progressors (n = 14). The hierarchical clustering of lipids was performed across all 733 samples analyzed. PC, phosphatidylcholine; lysoPC, lysophosphatidylcholine. (C) Blood glucose levels in 10-week-old female NOD progressors (n = 11) and non-progressors (n = 14) after 4 hr fast and 5 minutes after intraperitoneal (i.p.) glucose (1 g/kg) administration (2-way ANOVA with glucose administration and diabetes progression as factors, reported P-value for diabetes progression; error bars ± SEM). (D) Plasma insulin concentrations (mice and statistic same as in panel C). (E) There were no differences in body weight between the groups (mice and statistic same as in panel C). (F) Concentration of serum lysophosphatidylcholine (lysoPC; measured as total added concentration of PC(16∶0/0∶0) and PC(18∶0/0∶0)) in 8-week female NOD mice as dependent on diabetes progression and insulin autoantibody (IAA) positivity. Surrogate marker derived from lysoPC level and IAA positivity (Figure S2) was used to stratify mice according to diabetes risk in subsequent studies where mice were sacrificed for tissue-specific studies. Serum lipidomics, insulin, glucose, and body weight measurements were independently repeated in three independent studies (Studies 2–4; see Figures 6 and 7).
Figure 3
Figure 3. Progression of metabolic states in children who later progress to type 1 diabetes as compared to non-progressors, based on lipidomics data from an earlier study .
(A) Structure of the Hidden Markov Model (HMM) used in this study. The model is made to focus on progressive changes of lipidomic profiles over time by assuming that returning back in states is not possible after State 2. Separate HMM models were developed for progressors and non-progressors. The nodes in the graph represent the hidden states, each of which emits a multivariate profile of metabolite concentrations, while arrows represent possible transitions between the states. (B) HMM state progression as a function of age is similar for progressors and non-progressors. Each column shows the probabilities of being in the three states at a certain age, estimated by bootstrap. (C) Differences in lipidomic profiles (mean lipid concentrations) between progressors and non-progressors as a function of the progressive metabolic state, colored according to bootstrap-based confidence intervals.
Figure 4
Figure 4. Similarities between lipid changes observed in children who later progress to T1D and the early prediabetic changes present in female NOD mouse progressors.
(A) HMM state progression as a function of age in female NOD mice is similar for progressors and non-progressors. Each column shows the probabilities of being in the three states at a certain age, estimated by bootstrap. (B) Differences in lipidomic profiles (mean lipid concentrations) between progressors and non-progressors as a function of the progressive metabolic state, colored according to bootstrap-based confidence intervals. (C) Differences in lipid concentrations in diabetes progressors vs. non-progressors that generalize across the species. Mapping shown on the left is inferred from longitudinal lipidomic profiles from DIPP study children, including n = 56 progressors and n = 73 non-progressors (Figure 3), and NOD female mice (same as in Figure 2).
Figure 5
Figure 5. Female NOD mice at high risk of diabetes have more insulitis, elevated levels of insulinotropic amino acis in pancreatic islets, and diminished diversity of Clostridium leptum bacteria in caecum.
(A) Grading of pancreatic islet insulitis in normoglycemic 19-week-old female NOD mice comparing high- and low-risk groups. Insulitis was graded: 0, no visible infiltration, I peri-insulitis, II insulitis with <50% and III insulitis with >50% islet infiltration. 52 islets from four high-risk (11–17 islets/each) and 28 islets from three low-risk mice (7–10 islets/each) were graded. There was a tendency to more severe insulitis in the high-risk group (P = 0.07, χ2 test). Insulitis scoring was repeated in Study 4 (see Figure 6G). (B) Significantly regulated and selected other metabolites (P<0.07), out of 125 measured, in islets from female mice at high (HR) vs. low risk (LR) of developing diabetes (Study 2). Fourteen mice were 8 weeks old (two IAA+ LR, three IAA− LR, four IAA+ HR, five IAA− HR) and 11 were 19 weeks old (four IAA+ LR, three IAA− LR, one IAA+ HR, three IAA− HR) at time of sacrifice. FDR (Max. q-value at P<0.05) = 0.12. (C) Bacterial diversity of caecum samples from 19-week old female NOD mice, n = 4 from HR group and n = 7 from LR group, as detected with group specific DGGEs. Bifidobacteria did not amplify from any sample. Islet metabolomics and caecum DGGE analysis were performed once (i.e., only in NOD Study 2).
Figure 6
Figure 6. Markers of insulin resistance in 8–11 week old female NOD mice.
(A) Glucose-stimulated insulin secretion is elevated in the high-risk (HR) group (n = 18) as compared to low-risk (LR) group (n = 12) (measured in NOD Study 3). In the same mice, no significant differences between the HR and LR group were found in (B) glucose tolerance test (GTT) or (C) insulin tolerance test (ITT). (D) Homeostatic model assessment (HOMA-IR) index in LR (n = 13) and HR (n = 25) groups (Study 4), and GLUT4 expression in (E) muscle and (F) white adipose tissue (Study 4). (G) The HR mice at 10 weeks of age have slightly more insulitis. Total 678 islets from 8 LR mice (60–123 islets/each) and 633 islets from 8 HR mice (59–102 islets/each) were graded as in Figure 5A. (H) Plasma leptin (analyzed twice, in Studies 3 and 4, combined data analyzed; n = 24 for LR and n = 43 for HR) and (I) adiponectin (analyzed in Study 4; n = 14 for LR and n = 27 for HR) are elevated in 10-week-old HR mice. * indicates p<0.05.
Figure 7
Figure 7. Weight and adiposity in progression to autoimmune diabetes.
(A) Body weight (Study 4; n = 15 for lor-risk and n = 27 for high-risk mice). (B) Weight and (C) weight/body weight of gonadal white adipose tissue (Study 4). Correlations of (D) leptin and (E) adiponectin with weight of gonadal white adipose tissue. Adiposity was characterized in one independent study (Study 4).
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