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Song S, Zhang Q, Yu J. A mendelian randomization study investigating the causal relationships between 1400 serum metabolites and autoimmune diseases. Heliyon 2024; 10:e34560. [PMID: 39114021 PMCID: PMC11305314 DOI: 10.1016/j.heliyon.2024.e34560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 08/10/2024] Open
Abstract
Objective This study aims to explore the causal relationships between 1400 serum metabolites (SMs) and five autoimmune diseases (Myasthenia gravis [MG], Multiple sclerosis [MS], Systemic lupus erythematosus [SLE], Type 1 diabetes mellitus [T1DM], and Ulcerative colitis [UC]) through Mendelian randomization analysis. Method Data on MG, MS, SLE, T1DM, and UC were obtained from the IEU OpenGWAS Project database, while information on 1400 SMs was extracted from GWAS summary statistics provided by Chen et al. Causal relationships were assessed using the inverse variance weighted (IVW), MR-Egger, Weighted Median (WME), and Simple median (SME) methods. The robustness of instrumental variables was verified through computation of the F-statistic. Heterogeneity was evaluated using Cochran's Q test and the leave-one-out (LOO) method. Horizontal pleiotropy was assessed using MR-Egger regression and MR-PRESSO. Result Following correction of the IVW P values using the False Discovery Rate (FDR) method, it was found that increased levels of 5-methyluridine (ribothymidine) (OR = 1.191, 95%CI 1.086-1.307, FDR-P = 0.000) and 2'-deoxyuridine (OR = 1.337, 95%CI 1.127-1.586, FDR-P = 0.001) were found to be correlated with a higher risk of MS. Conversely, the ratio of S-adenosylhomocysteine (SAH) to 5-methyluridine (ribothymidine) (OR = 0.771, 95%CI 0.649-0.916, FDR-P = 0.007) was linked to a decreased risk of MS. Levels of 1,2-dilinoleoyl-GPE (18:2/18:2) (OR = 0.877, 95%CI 0.791-0.974, FDR-P = 0.003) appear to be a protective factor for T1DM. No notable correlations between SMs and MG, SLE, or UC. The study detected no heterogeneity or horizontal pleiotropy. Conclusion Levels of 5-methyluridine (ribothymidine), 2'-deoxyuridine, and the ratio of S-adenosylhomocysteine (SAH) to 5-methyluridine (ribothymidine) can serve as predictors for MS. Similarly, 1,2-dilinoleoyl-GPE (18:2/18:2) levels can be used to predict T1DM. However, no significant causal relationships were found between SMs and MG, SLE, or UC. This observation holds significant clinical implications for crafting tailored preventive and therapeutic approaches for ADs.
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Affiliation(s)
- Siyuan Song
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Qiling Zhang
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Jiangyi Yu
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
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Carry PM, Vanderlinden LA, Johnson RK, Buckner T, Steck AK, Kechris K, Yang IV, Fingerlin TE, Fiehn O, Rewers M, Norris JM. Longitudinal changes in DNA methylation during the onset of islet autoimmunity differentiate between reversion versus progression of islet autoimmunity. Front Immunol 2024; 15:1345494. [PMID: 38915393 PMCID: PMC11194352 DOI: 10.3389/fimmu.2024.1345494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 05/21/2024] [Indexed: 06/26/2024] Open
Abstract
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.
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Affiliation(s)
- Patrick M. Carry
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado, Aurora, CO, United States
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, CO, United States
| | | | - Randi K. Johnson
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Teresa Buckner
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
- Department of Kinesiology, Nutrition, and Dietetics, University of Northern Colorado, Greeley, CO, United States
| | - Andrea K. Steck
- Barbara Davis Center, Department of Pediatrics, University of Colorado, Aurora, CO, United States
| | - Katerina Kechris
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, CO, United States
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States
| | - Ivana V. Yang
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, CO, United States
- Department of Medicine, University of Colorado, Aurora, CO, United States
| | - Tasha E. Fingerlin
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States
- Department of Immunology and Genomic Medicine, National Jewish Health, Aurora, CO, United States
| | - Oliver Fiehn
- University of California Davis West Coast Metabolomics Center, Davis, CA, United States
| | - Marian Rewers
- Barbara Davis Center, Department of Pediatrics, University of Colorado, Aurora, CO, United States
| | - Jill M. Norris
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
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Ma W, Chen H, Hou H, Hu Q, Bai Y. TiO 2@COF-based solid-phase microextraction combined with UHPLC-MS/MS for the rapid determination of potential biomarkers of phosphatidylcholines and lysophosphatidylcholines in head and neck cancers. Anal Bioanal Chem 2023; 415:6771-6783. [PMID: 37776352 DOI: 10.1007/s00216-023-04954-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/07/2023] [Accepted: 09/13/2023] [Indexed: 10/02/2023]
Abstract
Phosphatidylcholine (PC) and lysophosphatidylcholine (LPC), two types of phospholipids (PLs), have been reported to be closely correlated with head and neck cancers of laryngeal cancer (LC) and thyroid cancer (TC), which make their analysis crucial. TiO2@COF-based solid-phase microextraction (SPME) coupled to UHPLC-MS/MS was developed for the rapid and accurate detection of seven potential PL biomarkers from small amounts of serum in this work. The combination of TiO2 and COF proves to be effective for the extraction of the target analytes. Under optimal conditions, the developed TiO2@COF-based SPME-UHPLC-MS/MS method revealed good linearity (R2 ≥ 0.997) with LODs ranging from 0.05 to 0.38 ng/mL for PLs, the extraction recoveries and matrix effects ranging from 83.09-112.03% and 85.38-113.67%, respectively. As a high-throughput pretreatment method, satisfactory probe-to-probe reproducibility rates of 2.7-10.1% were obtained. Finally, the TiO2@COF-based SPME-UHPLC-MS/MS method was applied to analyze LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 19:0, PC 16:0/18:1, and PC 18:0 in serum samples from early LC patients (n = 15), early TC patients (n = 15), and healthy volunteers (n = 15). The results indicated that cancer patients could be effectively differentiated from healthy controls using orthogonal partial least squares discriminant analysis (OPLS-DA). In conclusion, the established TiO2@COF-based SPME-UHPLC-MS/MS method is reliable for the rapid determination of the seven PLs in serum samples, which is promising for early diagnosis of head and neck cancers.
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Affiliation(s)
- Wanwan Ma
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
- Key Laboratory of Tobacco Biological Effects and Biosynthesis, China National Tobacco Quality Supervision and Test Center, Zhengzhou, 450001, China
- Key Laboratory of Tobacco Biological Effects and Biosynthesis, Beijing Life Science Academy, Beijing, 100101, China
| | - Huan Chen
- Key Laboratory of Tobacco Biological Effects and Biosynthesis, China National Tobacco Quality Supervision and Test Center, Zhengzhou, 450001, China
- Key Laboratory of Tobacco Biological Effects and Biosynthesis, Beijing Life Science Academy, Beijing, 100101, China
| | - Hongwei Hou
- Key Laboratory of Tobacco Biological Effects and Biosynthesis, China National Tobacco Quality Supervision and Test Center, Zhengzhou, 450001, China.
- Key Laboratory of Tobacco Biological Effects and Biosynthesis, Beijing Life Science Academy, Beijing, 100101, China.
| | - Qingyuan Hu
- Key Laboratory of Tobacco Biological Effects and Biosynthesis, China National Tobacco Quality Supervision and Test Center, Zhengzhou, 450001, China.
- Key Laboratory of Tobacco Biological Effects and Biosynthesis, Beijing Life Science Academy, Beijing, 100101, China.
| | - Yu Bai
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.
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Elhassan S, Dong F, Buckner T, Johnson RK, Seifert JA, Carry PM, Vanderlinden L, Waugh K, Rewers M, Norris JM. Investigating iron intake in risk of progression from islet autoimmunity to type 1 diabetes: The diabetes autoimmunity study in the young. Front Immunol 2023; 14:1124370. [PMID: 37056761 PMCID: PMC10086157 DOI: 10.3389/fimmu.2023.1124370] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Background Studies of the role of iron in the risk of type 1 diabetes (T1D) have been inconsistent. Given that iron generates reactive oxygen radicals, which can lead to oxidative damage and apoptosis in the beta cells of the pancreas, we examined whether iron intake was associated with the risk of progressing to T1D in individuals with islet autoimmunity (IA), the pre-clinical phase of T1D. Methods DAISY is a prospective cohort following 2,547 children at increased risk for IA and progression to T1D. IA is defined as at least two consecutive serum samples positive for at least one autoantibody (insulin, GAD, IA-2, or ZnT8). We measured dietary intake at the time of IA seroconversion in 175 children with IA, and of these, 64 progressed to T1D. We used Cox regression to examine the association between energy-adjusted iron intake and progression to T1D, adjusting for HLA-DR3/4 genotype, race/ethnicity, age at seroconversion, presence of multiple autoantibodies at seroconversion, and multiple vitamin use. In addition, we tested whether this association was modified by vitamin C or calcium intake. Results In children with IA, high iron intake (as defined as above the 75th percentile, > 20.3 mg/day) was associated with decreased risk of progression to T1D compared to moderate iron intake (as defined by the middle 25-75th percentiles, 12.7-20.3 mg/day) (adjusted hazard ratio (HR): 0.35; 95% confidence interval (CI): 0.15, 0.79). The association between iron intake and T1D was not modified by vitamin C nor calcium intake. In a sensitivity analysis, the removal of six children who had been diagnosed with celiac disease prior to IA seroconversion did not affect this association. Conclusion Higher iron intake at the time of IA seroconversion is associated with a lower risk of progression to T1D, independent of multivitamin supplement use. Further research that includes plasma biomarkers of iron status is needed to investigate the relationship between iron and the risk of T1D.
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Affiliation(s)
- Sulafa Elhassan
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Fran Dong
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Teresa Buckner
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Department of Kinesiology, Nutrition, and Dietetics, University of Northern Colorado, Greeley, CO, United States
| | - Randi K. Johnson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Jennifer A. Seifert
- Department of Medicine, Division of Rheumatology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Patrick M. Carry
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Lauren Vanderlinden
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Kathleen Waugh
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Marian Rewers
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Jill M. Norris
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
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Mønsted MØ, Bilgin M, Kuzma M, Pelantová H, Pedersen K, Tomášová P, Nazmutdinova A, Šedivá B, Funda D, Castro-Mejía JL, Holm LJ, Nielsen DS, Haupt-Jorgensen M. Reduced phosphatidylcholine level in the intestinal mucus layer of prediabetic NOD mice. APMIS 2023; 131:237-248. [PMID: 36811202 DOI: 10.1111/apm.13302] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 02/17/2023] [Indexed: 02/24/2023]
Abstract
Type 1 diabetes (T1D) is an autoimmune disease with rising incidence. Pre- and manifest T1D is associated with intestinal barrier dysfunction, skewed microbiota composition, and serum dyslipidemia. The intestinal mucus layer protects against pathogens and its structure and phosphatidylcholine (PC) lipid composition may be compromised in T1D, potentially contributing to barrier dysfunction. This study compared prediabetic Non-Obese Diabetic (NOD) mice to healthy C57BL/6 mice by analyzing the intestinal mucus PC profile by shotgun lipidomics, plasma metabolomics by mass spectrometry and nuclear magnetic resonance, intestinal mucus production by histology, and cecal microbiota composition by 16 S rRNA sequencing. Jejunal mucus PC class levels were decreased in early prediabetic NOD vs C57BL/6 mice. In colonic mucus of NOD mice, the level of several PC species was reduced throughout prediabetes. In plasma, similar reductions of PC species were observed in early prediabetic NOD mice, where also increased beta-oxidation was prominent. No histological alterations were found in jejunal nor colonic mucus between the mouse strains. However, the β-diversity of the cecal microbiota composition differed between prediabetic NOD and C57BL/6 mice, and the bacterial species driving this difference were related to decreased short-chain fatty acid (SCFA)-production in the NOD mice. This study reports reduced levels of PCs in the intestinal mucus layer and plasma of prediabetic NOD mice as well as reduced proportions of SCFA-producing bacteria in cecal content at early prediabetes, possibly contributing to intestinal barrier dysfunction and T1D.
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Affiliation(s)
- Mia Øgaard Mønsted
- Department of Pathology, Rigshospitalet, The Bartholin Institute, Copenhagen, Denmark
| | - Mesut Bilgin
- Lipidomics Core Facility, Cell Death and Metabolism Unit, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Marek Kuzma
- Institute of Microbiology, Czech Academy of Sciences, Prague, The Czech Republic
| | - Helena Pelantová
- Institute of Microbiology, Czech Academy of Sciences, Prague, The Czech Republic
| | - Kristina Pedersen
- Department of Pathology, Rigshospitalet, The Bartholin Institute, Copenhagen, Denmark
| | - Petra Tomášová
- Institute of Microbiology, Czech Academy of Sciences, Prague, The Czech Republic
| | | | - Blanka Šedivá
- Faculty of Applied Sciences, University of West Bohemia, Plzeň, The Czech Republic
| | - David Funda
- Institute of Microbiology, Czech Academy of Sciences, Prague, The Czech Republic
| | | | - Laurits Juulskov Holm
- Department of Pathology, Rigshospitalet, The Bartholin Institute, Copenhagen, Denmark
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Yue T, Tan H, Wang C, Liu Z, Yang D, Ding Y, Xu W, Yan J, Zheng X, Weng J, Luo S. High-risk genotypes for type 1 diabetes are associated with the imbalance of gut microbiome and serum metabolites. Front Immunol 2022; 13:1033393. [PMID: 36582242 PMCID: PMC9794034 DOI: 10.3389/fimmu.2022.1033393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
Background The profile of gut microbiota, serum metabolites, and lipids of type 1 diabetes (T1D) patients with different human leukocyte antigen (HLA) genotypes remains unknown. We aimed to explore gut microbiota, serum metabolites, and lipids signatures in individuals with T1D typed by HLA genotypes. Methods We did a cross-sectional study that included 73 T1D adult patients. Patients were categorized into two groups according to the HLA haplotypes they carried: those with any two of three susceptibility haplotypes (DR3, DR4, DR9) and without any of the protective haplotypes (DR8, DR11, DR12, DR15, DR16) were defined as high-risk HLA genotypes group (HR, n=30); those with just one or without susceptibility haplotypes as the non-high-risk HLA genotypes group (NHR, n=43). We characterized the gut microbiome profile with 16S rRNA gene amplicon sequencing and analyzed serum metabolites with liquid chromatography-mass spectrometry. Results Study individuals were 32.5 (8.18) years old, and 60.3% were female. Compared to NHR, the gut microbiota of HR patients were characterized by elevated abundances of Prevotella copri and lowered abundances of Parabacteroides distasonis. Differential serum metabolites (hypoxanthine, inosine, and guanine) which increased in HR were involved in purine metabolism. Different lipids, phosphatidylcholines and phosphatidylethanolamines, decreased in HR group. Notably, Parabacteroides distasonis was negatively associated (p ≤ 0.01) with hypoxanthine involved in purine metabolic pathways. Conclusions The present findings enabled a better understanding of the changes in gut microbiome and serum metabolome in T1D patients with HLA risk genotypes. Alterations of the gut microbiota and serum metabolites may provide some information for distinguishing T1D patients with different HLA risk genotypes.
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Affiliation(s)
- Tong Yue
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Huiling Tan
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Chaofan Wang
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Ziyu Liu
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Daizhi Yang
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yu Ding
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Wen Xu
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jinhua Yan
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xueying Zheng
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Jianping Weng
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China,*Correspondence: Jianping Weng, ; Sihui Luo,
| | - Sihui Luo
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China,*Correspondence: Jianping Weng, ; Sihui Luo,
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Vigers T, Vanderlinden LA, Johnson RK, Carry PM, Yang I, DeFelice BC, Kaizer AM, Pyle L, Rewers M, Fiehn O, Norris JM, Kechris K. A Mediation Approach to Discovering Causal Relationships between the Metabolome and DNA Methylation in Type 1 Diabetes. Metabolites 2021; 11:metabo11080542. [PMID: 34436483 PMCID: PMC8399445 DOI: 10.3390/metabo11080542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/06/2021] [Accepted: 08/10/2021] [Indexed: 11/16/2022] Open
Abstract
Environmental factors including viruses, diet, and the metabolome have been linked with the appearance of islet autoimmunity (IA) that precedes development of type 1 diabetes (T1D). We measured global DNA methylation (DNAm) and untargeted metabolomics prior to IA and at the time of seroconversion to IA in 92 IA cases and 91 controls from the Diabetes Autoimmunity Study in the Young (DAISY). Causal mediation models were used to identify seven DNAm probe-metabolite pairs in which the metabolite measured at IA mediated the protective effect of the DNAm probe measured prior to IA against IA risk. These pairs included five DNAm probes mediated by histidine (a metabolite known to affect T1D risk), one probe (cg01604946) mediated by phostidyl choline p-32:0 or o-32:1, and one probe (cg00390143) mediated by sphingomyelin d34:2. The top 100 DNAm probes were over-represented in six reactome pathways at the FDR <0.1 level (q = 0.071), including transport of small molecules and inositol phosphate metabolism. While the causal pathways in our mediation models require further investigation to better understand the biological mechanisms, we identified seven methylation sites that may improve our understanding of epigenetic protection against T1D as mediated by the metabolome.
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Affiliation(s)
- Tim Vigers
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Aurora, CO 80045, USA; (A.M.K.); (L.P.); (K.K.)
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, CO 80045, USA; (P.M.C.); (J.M.N.)
- Correspondence:
| | - Lauren A. Vanderlinden
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, CO 80045, USA; (L.A.V.); (M.R.)
| | - Randi K. Johnson
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado, Aurora, CO 80045, USA; (R.K.J.); (I.Y.)
| | - Patrick M. Carry
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, CO 80045, USA; (P.M.C.); (J.M.N.)
| | - Ivana Yang
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado, Aurora, CO 80045, USA; (R.K.J.); (I.Y.)
| | - Brian C. DeFelice
- West Coast Metabolomics Center, University of California, Davis, CA 95616, USA; (B.C.D.); (O.F.)
| | - Alexander M. Kaizer
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Aurora, CO 80045, USA; (A.M.K.); (L.P.); (K.K.)
| | - Laura Pyle
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Aurora, CO 80045, USA; (A.M.K.); (L.P.); (K.K.)
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, CO 80045, USA; (P.M.C.); (J.M.N.)
| | - Marian Rewers
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, CO 80045, USA; (L.A.V.); (M.R.)
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, CA 95616, USA; (B.C.D.); (O.F.)
| | - Jill M. Norris
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, CO 80045, USA; (P.M.C.); (J.M.N.)
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, CO 80045, USA; (L.A.V.); (M.R.)
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Aurora, CO 80045, USA; (A.M.K.); (L.P.); (K.K.)
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