Longitudinal Metabolome-Wide Signals Prior to the Appearance of a First Islet Autoantibody in Children Participating in the TEDDY Study

Longitudinal changes in DNA methylation during the onset of islet autoimmunity differentiate between reversion versus progression of islet autoimmunity

Background

Type 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers).

Methods

This epigenome-wide association study (EWAS) included longitudinal DNAm measurements in blood (Illumina 450K and EPIC) from participants in Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, one or more islet autoantibodies on at least two consecutive visits. We compared reverters - individuals who sero-reverted, negative for all autoantibodies on at least two consecutive visits and did not develop T1D (n=41); maintainers - continued to test positive for autoantibodies but did not develop T1D (n=60); progressors - developed clinical T1D (n=42). DNAm data were measured before (pre-SV visit) and after IA (post-SV visit). Linear mixed models were used to test for differences in pre- vs post-SV changes in DNAm across the three groups. Linear mixed models were also used to test for group differences in average DNAm. Cell proportions, age, and sex were adjusted for in all models. Median follow-up across all participants was 15.5 yrs. (interquartile range (IQR): 10.8-18.7).

Results

The median age at the pre-SV visit was 2.2 yrs. (IQR: 0.8-5.3) in progressors, compared to 6.0 yrs. (IQR: 1.3-8.4) in reverters, and 5.7 yrs. (IQR: 1.4-9.7) in maintainers. Median time between the visits was similar in reverters 1.4 yrs. (IQR: 1-1.9), maintainers 1.3 yrs. (IQR: 1.0-2.0), and progressors 1.8 yrs. (IQR: 1.0-2.0). Changes in DNAm, pre- vs post-SV, differed across the groups at one site (cg16066195) and 11 regions. Average DNAm (mean of pre- and post-SV) differed across 22 regions.

Conclusion

Differentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function.

Intake of B vitamins and the risk of developing islet autoimmunity and type 1 diabetes in the TEDDY study

PURPOSE

The aim was to study the association between dietary intake of B vitamins in childhood and the risk of islet autoimmunity (IA) and progression to type 1 diabetes (T1D) by the age of 10 years.

METHODS

We followed 8500 T1D-susceptible children born in the U.S., Finland, Sweden, and Germany in 2004 -2010 from the Environmental Determinants of Diabetes in the Young (TEDDY) study, which is a prospective observational birth cohort. Dietary intake of seven B vitamins was calculated from foods and dietary supplements based on 24-h recall at 3 months and 3-day food records collected regularly from 6 months to 10 years of age. Cox proportional hazard models were adjusted for energy, HLA-genotype, first-degree relative with T1D, sex, and country.

RESULTS

A total of 778 (9.2) children developed at least one autoantibody (any IA), and 335 (3.9%) developed multiple autoantibodies. 280 (3.3%) children had IAA and 319 (3.8%) GADA as the first autoantibody. 344 (44%) children with IA progressed to T1D. We observed that higher intake of niacin was associated with a decreased risk of developing multiple autoantibodies (HR 0.95; 95% CI 0.92, 0.98) per 1 mg/1000 kcal in niacin intake. Higher intake of pyridoxine (HR 0.66; 95% CI 0.46, 0.96) and vitamin B12 (HR 0.87; 95% CI 0.77, 0.97) was associated with a decreased risk of IAA-first autoimmunity. Higher intake of riboflavin (HR 1.38; 95% CI 1.05, 1.80) was associated with an increased risk of GADA-first autoimmunity. There were no associations between any of the B vitamins and the outcomes "any IA" and progression from IA to T1D.  CONCLUSION: In this multinational, prospective birth cohort of children with genetic susceptibility to T1D, we observed some direct and inverse associations between different B vitamins and risk of IA.

Possible heterogeneity of initial pancreatic islet beta-cell autoimmunity heralding type 1 diabetes

The etiology of type 1 diabetes (T1D) foreshadows the pancreatic islet beta-cell autoimmune pathogenesis that heralds the clinical onset of T1D. Standardized and harmonized tests of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A), and ZnT8 transporter (ZnT8A) allowed children to be followed from birth until the appearance of a first islet autoantibody. In the Environmental Determinants of Diabetes in the Young (TEDDY) study, a multicenter (Finland, Germany, Sweden, and the United States) observational study, children were identified at birth for the T1D high-risk HLA haploid genotypes DQ2/DQ8, DQ2/DQ2, DQ8/DQ8, and DQ4/DQ8. The TEDDY study was preceded by smaller studies in Finland, Germany, Colorado, Washington, and Sweden. The aims were to follow children at increased genetic risk to identify environmental factors that trigger the first-appearing autoantibody (etiology) and progress to T1D (pathogenesis). The larger TEDDY study found that the incidence rate of the first-appearing autoantibody was split into two patterns. IAA first peaked already during the first year of life and tapered off by 3-4 years of age. GADA first appeared by 2-3 years of age to reach a plateau by about 4 years. Prior to the first-appearing autoantibody, genetic variants were either common or unique to either pattern. A split was also observed in whole blood transcriptomics, metabolomics, dietary factors, and exposures such as gestational life events and early infections associated with prolonged shedding of virus. An innate immune reaction prior to the adaptive response cannot be excluded. Clarifying the mechanisms by which autoimmunity is triggered to either insulin or GAD65 is key to uncovering the etiology of autoimmune T1D.

Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review

BACKGROUND

Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed.

OBJECTIVE

We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction.

METHODS

We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes etiology on September 19th, 2022. Exposures were classified into specific external, general external, or internal exposures. We reviewed machine learning and data mining methods and characterized the scope of environmental exposures studied in the etiology of general diabetes, type 1 diabetes, type 2 diabetes, and other types of diabetes.

RESULTS

We identified 44 articles for inclusion. Specific external exposures were the most common exposures studied, and supervised models were the most common methods used. Well-established specific external exposures of low physical activity, high cholesterol, and high triglycerides were predictive of general diabetes, type 2 diabetes, and prediabetes, while novel metabolic and gut microbiome biomarkers were implicated in type 1 diabetes.

DISCUSSION

The use of machine learning and data mining methods to elucidate environmental triggers of diabetes was largely limited to well-established risk factors identified using easily explainable and interpretable models. Future studies should seek to leverage machine learning and data mining to explore the temporality and co-occurrence of multiple exposures and further evaluate the role of general external and internal exposures in diabetes etiology.

Vitamin C supplementation for diabetes management: A comprehensive narrative review

Growing evidence suggests that vitamin C supplementation may be an effective adjunct therapy in the management of people with diabetes. This paper critically reviews the current evidence on effects of vitamin C supplementation and its potential mechanisms in diabetes management. Evidence from meta-analyses of randomized controlled trials (RCTs) show favourable effects of vitamin C on glycaemic control and blood pressure that may be clinically meaningful, and mixed effects on blood lipids and endothelial function. However, evidence is mostly of low evidence certainty. Emerging evidence is promising for effects of vitamin C supplementation on some diabetes complications, particularly diabetic foot ulcers. However, there is a notable lack of robust and well-designed studies exploring effects of vitamin C as a single compound supplement on diabetes prevention and patient-important outcomes (i.e. prevention and amelioration of diabetes complications). RCTs are also required to investigate potential preventative or ameliorative effects of vitamin C on gestational diabetes outcomes. Oral vitamin C doses of 500-1000 mg per day are potentially effective, safe, and affordable for many individuals with diabetes. However, personalisation of supplementation regimens that consider factors such as vitamin C status, disease status, current glycaemic control, vitamin C intake, redox status, and genotype is important to optimize vitamin C's therapeutic effects safely. Finally, given a high prevalence of vitamin C deficiency in patients with complications, it is recommended that plasma vitamin C concentration be measured and monitored in the clinic setting.

Incomplete time-series gene expression in integrative study for islet autoimmunity prediction

Type 1 diabetes (T1D) outcome prediction plays a vital role in identifying novel risk factors, ensuring early patient care and designing cohort studies. TEDDY is a longitudinal cohort study that collects a vast amount of multi-omics and clinical data from its participants to explore the progression and markers of T1D. However, missing data in the omics profiles make the outcome prediction a difficult task. TEDDY collected time series gene expression for less than 6% of enrolled participants. Additionally, for the participants whose gene expressions are collected, 79% time steps are missing. This study introduces an advanced bioinformatics framework for gene expression imputation and islet autoimmunity (IA) prediction. The imputation model generates synthetic data for participants with partially or entirely missing gene expression. The prediction model integrates the synthetic gene expression with other risk factors to achieve better predictive performance. Comprehensive experiments on TEDDY datasets show that: (1) Our pipeline can effectively integrate synthetic gene expression with family history, HLA genotype and SNPs to better predict IA status at 2 years (sensitivity 0.622, AUC 0.715) compared with the individual datasets and state-of-the-art results in the literature (AUC 0.682). (2) The synthetic gene expression contains predictive signals as strong as the true gene expression, reducing reliance on expensive and long-term longitudinal data collection. (3) Time series gene expression is crucial to the proposed improvement and shows significantly better predictive ability than cross-sectional gene expression. (4) Our pipeline is robust to limited data availability. Availability: Code is available at https://github.com/compbiolabucf/TEDDY.

Exposure to persistent organic pollutants alters the serum metabolome in non-obese diabetic mice

INTRODUCTION

Autoimmune disorders such as type 1 diabetes (T1D) are believed to be caused by the interplay between several genetic and environmental factors. Elucidation of the role of environmental factors in metabolic and immune dysfunction leading to autoimmune disease is not yet well characterized.

OBJECTIVES

Here we investigated the impact of exposure to a mixture of persistent organic pollutants (POPs) on the metabolome in non-obese diabetic (NOD) mice, an experimental model of T1D. The mixture contained organochlorides, organobromides, and per- and polyfluoroalkyl substances (PFAS).

METHODS

Analysis of molecular lipids (lipidomics) and bile acids in serum samples was performed by UPLC-Q-TOF/MS, while polar metabolites were analyzed by GC-Q-TOF/MS.

RESULTS

Experimental exposure to the POP mixture in these mice led to several metabolic changes, which were similar to those previously reported as associated with PFAS exposure, as well as risk of T1D in human studies. This included an increase in the levels of sugar derivatives, triacylglycerols and lithocholic acid, and a decrease in long chain fatty acids and several lipid classes, including phosphatidylcholines, lysophosphatidylcholines and sphingomyelins.

CONCLUSION

Taken together, our study demonstrates that exposure to POPs results in an altered metabolic signature previously associated with autoimmunity.

Dysregulation of secondary bile acid metabolism precedes islet autoimmunity and type 1 diabetes

The gut microbiota is crucial in the regulation of bile acid (BA) metabolism. However, not much is known about the regulation of BAs during progression to type 1 diabetes (T1D). Here, we analyzed serum and stool BAs in longitudinal samples collected at 3, 6, 12, 18, 24, and 36 months of age from children who developed a single islet autoantibody (AAb) (P1Ab; n = 23) or multiple islet AAbs (P2Ab; n = 13) and controls (CTRs; n = 38) who remained AAb negative. We also analyzed the stool microbiome in a subgroup of these children. Factor analysis showed that age had the strongest impact on both BA and microbiome profiles. We found that at an early age, systemic BAs and microbial secondary BA pathways were altered in the P2Ab group compared with the P1Ab and CTR groups. Our findings thus suggest that dysregulated BA metabolism in early life may contribute to the risk and pathogenesis of T1D.

Integration of Infant Metabolite, Genetic, and Islet Autoimmunity Signatures to Predict Type 1 Diabetes by Age 6 Years

CONTEXT

Biomarkers that can accurately predict risk of type 1 diabetes (T1D) in genetically predisposed children can facilitate interventions to delay or prevent the disease.

OBJECTIVE

This work aimed to determine if a combination of genetic, immunologic, and metabolic features, measured at infancy, can be used to predict the likelihood that a child will develop T1D by age 6 years.

METHODS

Newborns with human leukocyte antigen (HLA) typing were enrolled in the prospective birth cohort of The Environmental Determinants of Diabetes in the Young (TEDDY). TEDDY ascertained children in Finland, Germany, Sweden, and the United States. TEDDY children were either from the general population or from families with T1D with an HLA genotype associated with T1D specific to TEDDY eligibility criteria. From the TEDDY cohort there were 702 children will all data sources measured at ages 3, 6, and 9 months, 11.4% of whom progressed to T1D by age 6 years. The main outcome measure was a diagnosis of T1D as diagnosed by American Diabetes Association criteria.

RESULTS

Machine learning-based feature selection yielded classifiers based on disparate demographic, immunologic, genetic, and metabolite features. The accuracy of the model using all available data evaluated by the area under a receiver operating characteristic curve is 0.84. Reducing to only 3- and 9-month measurements did not reduce the area under the curve significantly. Metabolomics had the largest value when evaluating the accuracy at a low false-positive rate.

CONCLUSION

The metabolite features identified as important for progression to T1D by age 6 years point to altered sugar metabolism in infancy. Integrating this information with classic risk factors improves prediction of the progression to T1D in early childhood.

Impact of Extensively Hydrolyzed Infant Formula on Circulating Lipids During Early Life

Background

Current evidence suggests that the composition of infant formula (IF) affects the gut microbiome, intestinal function, and immune responses during infancy. However, the impact of IF on circulating lipid profiles in infants is still poorly understood. The objectives of this study were to (1) investigate how extensively hydrolyzed IF impacts serum lipidome compared to conventional formula and (2) to associate changes in circulatory lipids with gastrointestinal biomarkers including intestinal permeability.

Methods

In a randomized, double-blind controlled nutritional intervention study (n = 73), we applied mass spectrometry-based lipidomics to analyze serum lipids in infants who were fed extensively hydrolyzed formula (HF) or conventional, regular formula (RF). Serum samples were collected at 3, 9, and 12 months of age. Child’s growth (weight and length) and intestinal functional markers, including lactulose mannitol (LM) ratio, fecal calprotectin, and fecal beta-defensin, were also measured at given time points. At 3 months of age, stool samples were analyzed by shotgun metagenomics.

Results

Concentrations of sphingomyelins were higher in the HF group as compared to the RF group. Triacylglycerols (TGs) containing saturated and monounsaturated fatty acyl chains were found in higher levels in the HF group at 3 months, but downregulated at 9 and 12 months of age. LM ratio was lower in the HF group at 9 months of age. In the RF group, the LM ratio was positively associated with ether-linked lipids. Such an association was, however, not observed in the HF group.

Conclusion

Our study suggests that HF intervention changes the circulating lipidome, including those lipids previously found to be associated with progression to islet autoimmunity or overt T1D.

Clinical Trial Registration

[Clinicaltrials.gov], identifier [NCT01735123].

Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms

PURPOSE OF REVIEW

Metabolomics enables rapid interrogation of widespread metabolic processes making it well suited for studying diabetes. Here, we review the current status of metabolomic investigation in diabetes, highlighting its applications for improving risk prediction and mechanistic understanding.

RECENT FINDINGS

Findings of metabolite associations with type 2 diabetes risk have confirmed experimental observations (e.g., branched-chain amino acids) and also pinpointed novel pathways of diabetes risk (e.g., dimethylguanidino valeric acid). In type 1 diabetes, abnormal metabolite patterns are observed prior to the development of autoantibodies and hyperglycemia. Diabetes complications display specific metabolite signatures that are distinct from the metabolic derangements of diabetes and differ across vascular beds. Lastly, metabolites respond acutely to pharmacologic treatment, providing opportunities to understand inter-individual treatment responses. Metabolomic studies have elucidated biological mechanisms underlying diabetes development, complications, and therapeutic response. While not yet ready for clinical translation, metabolomics is a powerful and promising precision medicine tool.

Enteroviruses and Type 1 Diabetes: Multiple Mechanisms and Factors?

Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by insulin deficiency and resultant hyperglycemia. Complex interactions of genetic and environmental factors trigger the onset of autoimmune mechanisms responsible for development of autoimmunity to β cell antigens and subsequent development of T1D. A potential role of virus infections has long been hypothesized, and growing evidence continues to implicate enteroviruses as the most probable triggering viruses. Recent studies have strengthened the association between enteroviruses and development of autoimmunity in T1D patients, potentially through persistent infections. Enterovirus infections may contribute to different stages of disease development. We review data from both human cohort studies and experimental research exploring the potential roles and molecular mechanisms by which enterovirus infections can impact disease outcome.

Enteroviruses and Type 1 Diabetes: Multiple Mechanisms and Factors?

Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by insulin deficiency and resultant hyperglycemia. Complex interactions of genetic and environmental factors trigger the onset of autoimmune mechanisms responsible for development of autoimmunity to β cell antigens and subsequent development of T1D. A potential role of virus infections has long been hypothesized, and growing evidence continues to implicate enteroviruses as the most probable triggering viruses. Recent studies have strengthened the association between enteroviruses and development of autoimmunity in T1D patients, potentially through persistent infections. Enterovirus infections may contribute to different stages of disease development. We review data from both human cohort studies and experimental research exploring the potential roles and molecular mechanisms by which enterovirus infections can impact disease outcome.

Untargeted metabolomics reveals gender- and age- independent metabolic changes of type 1 diabetes in Chinese children

INTRODUCTION

Type 1 diabetes (T1D) is a chronic condition associated with multiple complications that substantially affect both the quality of life and the life-span of children. Untargeted Metabolomics has provided new insights into disease pathogenesis and risk assessment.

METHODS

In this study, we characterized the serum metabolic profiles of 76 children with T1D and 65 gender- and age- matched healthy controls using gas chromatography coupled with timeof-flight mass spectrometry. In parallel, we comprehensively evaluated the clinical phenome of T1D patients, including routine blood and urine tests, and concentrations of cytokines, hormones, proteins, and trace elements.

RESULTS

A total of 70 differential metabolites covering 11 metabolic pathways associated with T1D were identified, which were mainly carbohydrates, indoles, unsaturated fatty acids, amino acids, and organic acids. Subgroup analysis revealed that the metabolic changes were consistent among pediatric patients at different ages or gender but were closely associated with the duration of the disease.

DISCUSSION

Carbohydrate metabolism, unsaturated fatty acid biosynthesis, and gut microbial metabolism were identified as distinct metabolic features of pediatric T1D. These metabolic changes were also associated with T1D, which may provide important insights into the pathogenesis of the complications associated with diabetes.

Lipidomic Analyses Reveal Modulation of Lipid Metabolism by the PFAS Perfluoroundecanoic Acid (PFUnDA) in Non-Obese Diabetic Mice

Exposure to Per- and polyfluoroalkyl substances (PFAS) has been linked to multiple undesirable health outcomes across a full lifespan, both in animal models as well as in human epidemiological studies. Immunosuppressive effects of PFAS have been reported, including increased risk of infections and suppressed vaccination responses in early childhood, as well as association with immunotoxicity and diabetes. On a mechanistic level, PFAS exposure has been linked with metabolic disturbances, particularly in lipid metabolism, but the underlying mechanisms are poorly characterized. Herein we explore lipidomic signatures of prenatal and early-life exposure to perfluoroundecanoic acid (PFUnDA) in non-obese diabetic (NOD) mice; an experimental model of autoimmune diabetes. Female NOD mice were exposed to four levels of PFUnDA in drinking water at mating, during gestation and lactation, and during the first weeks of life of female offspring. At offspring age of 11–12 weeks, insulitis and immunological endpoints were assessed, and serum samples were collected for comprehensive lipidomic analyses. We investigated the associations between exposure, lipidomic profile, insulitis grade, number of macrophages and apoptotic, active-caspase-3-positive cells in pancreatic islets. Dose-dependent changes in lipidomic profiles in mice exposed to PFUnDA were observed, with most profound changes seen at the highest exposure levels. Overall, PFUnDA exposure caused downregulation of phospholipids and triacylglycerols containing polyunsaturated fatty acids. Our results show that PFUnDA exposure in NOD mice alters lipid metabolism and is associated with pancreatic insulitis grade. Moreover, the results are in line with those reported in human studies, thus suggesting NOD mice as a suitable model to study the impacts of environmental chemicals on T1D.

Virus Infection Is an Instigator of Intestinal Dysbiosis Leading to Type 1 Diabetes

In addition to genetic predisposition, environmental determinants contribute to a complex etiology leading to onset of type 1 diabetes (T1D). Multiple studies have established the gut as an important site for immune modulation that can directly impact development of autoreactive cell populations against pancreatic self-antigens. Significant efforts have been made to unravel how changes in the microbiome function as a contributor to autoimmune responses and can serve as a biomarker for diabetes development. Large-scale longitudinal studies reveal that common environmental exposures precede diabetes pathology. Virus infections, particularly those associated with the gut, have been prominently identified as risk factors for T1D development. Evidence suggests recent-onset T1D patients experience pre-existing subclinical enteropathy and dysbiosis leading up to development of diabetes. The start of these dysbiotic events coincide with detection of virus infections. Thus viral infection may be a contributing driver for microbiome dysbiosis and disruption of intestinal homeostasis prior to T1D onset. Ultimately, understanding the cross-talk between viral infection, the microbiome, and the immune system is key for the development of preventative measures against T1D.

Knockout of the Amino Acid Transporter SLC6A19 and Autoimmune Diabetes Incidence in Female Non-Obese Diabetic (NOD) Mice

High protein feeding has been shown to accelerate the development of type 1 diabetes in female non-obese diabetic (NOD) mice. Here, we investigated whether reducing systemic amino acid availability via knockout of the Slc6a19 gene encoding the system B(0) neutral amino acid transporter AT1 would reduce the incidence or delay the onset of type 1 diabetes in female NOD mice. Slc6a19 gene deficient NOD mice were generated using the CRISPR-Cas9 system which resulted in marked aminoaciduria. The incidence of diabetes by week 30 was 59.5% (22/37) and 69.0% (20/29) in NOD.Slc6a19^+/+ and NOD.Slc6a19^-/- mice, respectively (hazard ratio 0.77, 95% confidence interval 0.41-1.42; Mantel-Cox log rank test: p = 0.37). The median survival time without diabetes was 28 and 25 weeks for NOD.Slc6a19^+/+ and NOD.Slc6a19^-/- mice, respectively (ratio 1.1, 95% confidence interval 0.6-2.0). Histological analysis did not show differences in islet number or the degree of insulitis between wild type and Slc6a19 deficient NOD mice. We conclude that Slc6a19 deficiency does not prevent or delay the development of type 1 diabetes in female NOD mice.

Prediction of Type 1 Diabetes at Birth: Cord Blood Metabolites vs Genetic Risk Score in the Norwegian Mother, Father, and Child Cohort

BACKGROUND AND AIM

Genetic markers are established as predictive of type 1 diabetes, but unknown early life environment is believed to be involved. Umbilical cord blood may reflect perinatal metabolism and exposures. We studied whether selected polar metabolites in cord blood contribute to prediction of type 1 diabetes.

METHODS

Using a targeted UHPLC-QQQ-MS platform, we quantified 27 low-molecular-weight metabolites (including amino acids, small organic acids, and bile acids) in 166 children, who later developed type 1 diabetes, and 177 random control children in the Norwegian Mother, Father, and Child cohort. We analyzed the data using logistic regression (estimating odds ratios per SD [adjusted odds ratio (aOR)]), area under the receiver operating characteristic curve (AUC), and k-means clustering. Metabolites were compared to a genetic risk score based on 51 established non-HLA single-nucleotide polymorphisms, and a 4-category HLA risk group.

RESULTS

The strongest associations for metabolites were aminoadipic acid (aOR = 1.23; 95% CI, 0.97-1.55), indoxyl sulfate (aOR = 1.15; 95% CI, 0.87-1.51), and tryptophan (aOR = 0.84; 95% CI, 0.65-1.10), with other aORs close to 1.0, and none significantly associated with type 1 diabetes. K-means clustering identified 6 clusters, none of which were associated with type 1 diabetes. Cross-validated AUC showed no predictive value of metabolites (AUC 0.49), whereas the non-HLA genetic risk score AUC was 0.56 and the HLA risk group AUC was 0.78.

CONCLUSIONS

In this large study, we found no support of a predictive role of cord blood concentrations of selected bile acids and other small polar metabolites in the development of type 1 diabetes.

Pharmacometabolomics identifies candidate predictor metabolites of an L-carnitine treatment mortality benefit in septic shock

Sepsis‐induced metabolic dysfunction contributes to organ failure and death. L‐carnitine has shown promise for septic shock, but a recent phase II study of patients with vasopressor‐dependent septic shock demonstrated a non‐significant reduction in mortality. We undertook a pharmacometabolomics study of these patients (n = 250) to identify metabolic profiles predictive of a 90‐day mortality benefit from L‐carnitine. The independent predictive value of each pretreatment metabolite concentration, adjusted for L‐carnitine dose, on 90‐day mortality was determined by logistic regression. A grid‐search analysis maximizing the Z‐statistic from a binomial proportion test identified specific metabolite threshold levels that discriminated L‐carnitine responsive patients. Threshold concentrations were further assessed by hazard ratio and Kaplan‐Meier estimate. Accounting for L‐carnitine treatment and dose, 11 ¹H‐NMR metabolites and 12 acylcarnitines were independent predictors of 90‐day mortality. Based on the grid‐search analysis numerous acylcarnitines and valine were identified as candidate metabolites of drug response. Acetylcarnitine emerged as highly viable for the prediction of an L‐carnitine mortality benefit due to its abundance and biological relevance. Using its most statistically significant threshold concentration, patients with pretreatment acetylcarnitine greater than or equal to 35 µM were less likely to die at 90 days if treated with L‐carnitine (18 g) versus placebo (p = 0.01 by log rank test). Metabolomics also identified independent predictors of 90‐day sepsis mortality. Our proof‐of‐concept approach shows how pharmacometabolomics could be useful for tackling the heterogeneity of sepsis and informing clinical trial design. In addition, metabolomics can help understand mechanisms of sepsis heterogeneity and variable drug response, because sepsis induces alterations in numerous metabolite concentrations.

Phospholipid Levels at Seroconversion Are Associated With Resolution of Persistent Islet Autoimmunity: The Diabetes Autoimmunity Study in the Young

Reversion of islet autoimmunity (IA) may point to mechanisms that prevent IA progression. We followed 199 individuals who developed IA during the Diabetes Autoimmunity Study in the Young. Untargeted metabolomics was performed in serum samples following IA. Cox proportional hazards models were used to test whether the metabolites (2,487) predicted IA reversion: two or more consecutive visits negative for all autoantibodies. We conducted a principal components analysis (PCA) of the top metabolites; |hazard ratio (HR) >1.25| and nominal P < 0.01. Phosphatidylcholine (16:0_18:1(9Z)) was the strongest individual metabolite (HR per 1 SD 2.16, false discovery rate (FDR)-adjusted P = 0.0037). Enrichment analysis identified four clusters (FDR P < 0.10) characterized by an overabundance of sphingomyelin (d40:0), phosphatidylcholine (16:0_18:1(9Z)), phosphatidylcholine (30:0), and l-decanoylcarnitine. Overall, 63 metabolites met the criteria for inclusion in the PCA. PC1 (HR 1.4, P < 0.0001), PC2 (HR 0.85, P = 0.0185), and PC4 (HR 1.28, P = 0.0103) were associated with IA reversion. Given the potential influence of diet on the metabolome, we investigated whether nutrients were correlated with PCs. We identified 20 nutrients that were correlated with the PCs (P < 0.05). Total sugar intake was the top nutrient. Overall, we identified an association between phosphatidylcholine, sphingomyelin, and carnitine levels and reversion of IA.

Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression

Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progression to T1D onset remain elusive. The Environmental Determinants of Diabetes in the Young (TEDDY) consortium is a prospective cohort study aiming to determine genetic and environmental interactions causing T1D. Here, we analyzed longitudinal blood transcriptomes of 2013 samples from 400 individuals in the TEDDY study before both T1D and islet autoimmunity. We identified and interpreted age-associated gene expression changes in healthy infancy and age-independent changes tracking with progression to both T1D and islet autoimmunity, beginning before other evidence of islet autoimmunity was present. We combined multivariate longitudinal data in a Bayesian joint model to predict individual risk of T1D onset and validated the association of a natural killer cell signature with progression and the model's predictive performance on an additional 356 samples from 56 individuals in the independent Type 1 Diabetes Prediction and Prevention study. Together, our results indicate that T1D is characterized by early and longitudinal changes in gene expression, informing the immunopathology of disease progression and facilitating prediction of its course.

Nuclear magnetic resonance-based metabolomic analysis in the assessment of preclinical atherosclerosis in type 1 diabetes and preeclampsia

AIMS

Evaluate the role of plasma metabolomics in atherosclerosis according to the presence of type 1 diabetes (T1D) or previous preeclampsia.

METHODS

We recruited 105 women without cardiovascular disease and last pregnancy ≥5 years previously, divided according to the presence of T1D or previous preeclampsia. Preclinical atherosclerosis was defined as the presence of carotid plaque (intima-media thickness ≥1.5 mm) assessed by ultrasonography. Metabolomics were evaluated by nuclear magnetic resonance (NMR). Bivariate and multivariate-adjusted differences in NMR-metabolomics were evaluated.

RESULTS

The participants were 44.9 ± 8.1 years-old; 20% harbored plaques. There were significant differences in lipidic-, energetic- and nitrogen-related metabolites according to the presence of T1D/preeclampsia (p < 0.05). In multivariate-adjusted models (by age, statins, blood pressure and T1D/preeclampsia), only lipidomic-related metabolites were associated with atherosclerosis in the whole sample. However, stronger associations were observed in women with previous preeclampsia (vs. without; per 0.5 mmol/L increments); phosphatidylcholine, OR 4.08 (1.32-27.22); free cholesterol, 5.18 (1.22-21.97); saturated fatty acids, OR 2.99 (1.37-6.48); w-7, OR 2.29 (1.15-4.56); and w-9 fatty acids, OR 1.49 (1.00-2.23).

CONCLUSIONS

NMR-metabolomics showed a differential pattern according to the presence of T1D/preeclampsia in relation to preclinical atherosclerosis. Since most of these metabolites mirror lifestyle factors, they could help tailor dietetic advice in high-risk women.

Plasma Metabolome and Circulating Vitamins Stratified Onset Age of an Initial Islet Autoantibody and Progression to Type 1 Diabetes: The TEDDY Study

Children's plasma metabolome, especially lipidome, reflects gene regulation and dietary exposures, heralding the development of islet autoantibodies (IA) and type 1 diabetes (T1D). The Environmental Determinants of Diabetes in the Young (TEDDY) study enrolled 8,676 newborns by screening of HLA-DR-DQ genotypes at six clinical centers in four countries, profiled metabolome, and measured concentrations of ascorbic acid, 25-hydroxyvitamin D [25(OH)D], and erythrocyte membrane fatty acids following birth until IA seroconversion under a nested case-control design. We grouped children having an initial autoantibody only against insulin (IAA-first) or GAD (GADA-first) by unsupervised clustering of temporal lipidome, identifying a subgroup of children having early onset of each initial autoantibody, i.e., IAA-first by 12 months and GADA-first by 21 months, consistent with population-wide early seroconversion age. Differential analysis showed that infants having reduced plasma ascorbic acid and cholesterol experienced IAA-first earlier, while early onset of GADA-first was preceded by reduced sphingomyelins at infancy. Plasma 25(OH)D prior to either autoantibody was lower in T1D progressors compared with nonprogressors, with simultaneous lower diglycerides, lysophosphatidylcholines, triglycerides, and alanine before GADA-first. Plasma ascorbic acid and 25(OH)D at infancy were lower in HLA-DR3/DR4 children among IA case subjects but not in matched control subjects, implying gene expression dysregulation of circulating vitamins as latent signals for IA or T1D progression.

Metabolomics-related nutrient patterns at seroconversion and risk of progression to type 1 diabetes

OBJECTIVE

Our aim was to elucidate the role of diet in type 1 diabetes (T1D) by examining combinations of nutrient intake in the progression from islet autoimmunity (IA) to T1D.

METHODS

We measured 2457 metabolites and dietary intake at the time of seroconversion in 132 IA-positive children in the prospective Diabetes Autoimmunity Study in the Young. IA was defined as the first of two consecutive visits positive for at least one autoantibody (insulin, GAD, IA-2, or ZnT8). By December 2018, 40 children progressed to T1D. Intakes of 38 nutrients were estimated from semiquantitative food frequency questionnaires. We tested the association of each metabolite with progression to T1D using multivariable Cox regression. Nutrient patterns that best explained variation in candidate metabolites were identified using reduced rank regression (RRR), and their association with progression to T1D was tested using Cox regression adjusting for age at seroconversion and high-risk HLA genotype.

RESULTS

In stepwise selection, 22 nutrients significantly predicted at least two of the 13 most significant metabolites associated with progression to T1D, and were included in RRR. A nutrient pattern corresponding to intake lower in linoleic acid, niacin, and riboflavin, and higher in total sugars, explained 18% of metabolite variability. Children scoring higher on this metabolite-related nutrient pattern at seroconversion had increased risk for progressing to T1D (HR = 3.17, 95%CI = 1.42-7.05).

CONCLUSIONS

Combinations of nutrient intake reflecting candidate metabolites are associated with increased risk of T1D, and may help focus dietary prevention efforts.