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Ottosson F, Russo F, Abrahamsson A, MacSween N, Courraud J, Skogstrand K, Melander O, Ericson U, Orho-Melander M, Cohen AS, Grove J, Mortensen PB, Hougaard DM, Ernst M. Unraveling the metabolomic architecture of autism in a large Danish population-based cohort. BMC Med 2024; 22:302. [PMID: 39026322 PMCID: PMC11264881 DOI: 10.1186/s12916-024-03516-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND The prevalence of autism in Denmark has been increasing, reaching 1.65% among 10-year-old children, and similar trends are seen elsewhere. Although there are several factors associated with autism, including genetic, environmental, and prenatal factors, the molecular etiology of autism is largely unknown. Here, we use untargeted metabolomics to characterize the neonatal metabolome from dried blood spots collected shortly after birth. METHODS We analyze the metabolomic profiles of a subset of a large Danish population-based cohort (iPSYCH2015) consisting of over 1400 newborns, who later are diagnosed with autism and matching controls and in two Swedish population-based cohorts comprising over 7000 adult participants. Mass spectrometry analysis was performed by a timsTOF Pro operated in QTOF mode, using data-dependent acquisition. By applying an untargeted metabolomics approach, we could reproducibly measure over 800 metabolite features. RESULTS We detected underlying molecular perturbations across several metabolite classes that precede autism. In particular, the cyclic dipeptide cyclo-leucine-proline (FDR-adjusted p = 0.003) and the carnitine-related 5-aminovaleric acid betaine (5-AVAB) (FDR-adjusted p = 0.03), were associated with an increased probability for autism, independently of known prenatal and genetic risk factors. Analysis of genetic and dietary data in adults revealed that 5-AVAB was associated with increased habitual dietary intake of dairy (FDR-adjusted p < 0.05) and with variants near SLC22A4 and SLC22A5 (p < 5.0e - 8), coding for a transmembrane carnitine transporter protein involved in controlling intracellular carnitine levels. CONCLUSIONS Cyclo-leucine-proline and 5-AVAB are associated with future diagnosis of autism in Danish neonates, both representing novel early biomarkers for autism. 5-AVAB is potentially modifiable and may influence carnitine homeostasis.
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Affiliation(s)
- Filip Ottosson
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
| | - Francesco Russo
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Anna Abrahamsson
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Nadia MacSween
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Julie Courraud
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, 15771, Panepistimiopolis, ZografouAthens, Greece
- Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 11528, Athens, Greece
| | - Kristin Skogstrand
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Olle Melander
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ulrika Ericson
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | - Arieh S Cohen
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Testcenter Denmark, Statens Serum Institut, Copenhagen, Denmark
| | - Jakob Grove
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Center, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- NCRR - National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- CIRRAU - Centre for Integrated Registerbased Research at Aarhus University, Aarhus, Denmark
| | - David M Hougaard
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Madeleine Ernst
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
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Dai X, Wang H, Zhong R, Li J, Hou Y. Causality of genetically determined metabolites on susceptibility to prevalent urological cancers: a two-sample Mendelian randomization study and meta-analysis. Front Genet 2024; 15:1398165. [PMID: 39011400 PMCID: PMC11246892 DOI: 10.3389/fgene.2024.1398165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 06/14/2024] [Indexed: 07/17/2024] Open
Abstract
Background Prevalent urological cancers, including kidney, prostate, bladder, and testicular cancers, contribute significantly to global cancer incidence and mortality. Metabolomics, focusing on small-molecule intermediates, has emerged as a tool to understand cancer etiology. Given the knowledge gap in this field, we employ a two-sample Mendelian randomization (MR) analysis to investigate the causal relationships between genetically determined metabolites (GDMs) and the susceptibility to four common urological cancers. Methods The study employs genome-wide association studies (GWAS) data from European populations, featuring the most extensive case count available for both blood metabolites and four prevalent urological cancers. Preliminary and secondary MR analyses were separately conducted, employing inverse variance weighted (IVW) as the primary method. Multiple statistical analyses, including the MR-Steiger test, Cochran's Q test, leave-one-out analysis, MR-Egger intercept analysis, and MR-PRESSO analysis, were executed to ensure robustness. Additionally, a meta-analysis was carried out to consolidate findings. The weighted median (WM) method was utilized for a relatively lenient correction (PWM < 0.05). Results After rigorous genetic variation filtering, 645 out of 1,400 metabolites were included in both preliminary and secondary MR analyses. Preliminary MR analysis identified 96 potential causal associations between 94 distinct metabolites and four urological cancers. Secondary analysis based on Finnish outcome data revealed 93 potential causal associations. Cross-database meta-analysis identified 68 blood metabolites associated with four urological cancers. Notably, 31 metabolites remained significant after using WM for correction, with additional 37 suggestive causal relationships. Reverse MR analysis revealed a significant causal association between genetically predicted prostate cancer and elevated 4-hydroxychlorothalonil levels (IVW, combined OR: 1.039, 95% CI 1.014-1.064, p = 0.002; WM, combined OR: 1.052, 95% CI 1.010-1.095, p = 0.014). Conclusion This comprehensive MR study provides insights into the causal relationships between blood metabolites and urological cancers, revealing potential biomarkers and therapeutic targets, thereby addressing gaps in understanding and laying the foundation for targeted interventions in urological cancer research and treatment.
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Affiliation(s)
- Xianyu Dai
- Urology Department, First Hospital of Jilin University, Changchun, China
| | - Hongjie Wang
- Urology Department, First Hospital of Jilin University, Changchun, China
| | - Rong Zhong
- Urology Department, First Hospital of Jilin University, Changchun, China
| | - Jiajun Li
- Department of Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yuchuan Hou
- Urology Department, First Hospital of Jilin University, Changchun, China
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Laboulaye R, Borda V, Chen S, North KE, Kaplan R, O'Connor TD. ClOneHORT: Approaches for Improved Fidelity in Generative Models of Synthetic Genomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600651. [PMID: 38979338 PMCID: PMC11230377 DOI: 10.1101/2024.06.25.600651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Motivation Deep generative models have the potential to overcome difficulties in sharing individual-level genomic data by producing synthetic genomes that preserve the genomic associations specific to a cohort while not violating the privacy of any individual cohort member. However, there is significant room for improvement in the fidelity and usability of existing synthetic genome approaches. Results We demonstrate that when combined with plentiful data and with population-specific selection criteria, deep generative models can produce synthetic genomes and cohorts that closely model the original populations. Our methods improve fidelity in the site-frequency spectra and linkage disequilibrium decay and yield synthetic genomes that can be substituted in downstream local ancestry inference analysis, recreating results with .91 to .94 accuracy. Availability The model described in this paper is freely available at github.com/rlaboulaye/clonehort .
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Bustamante M, Balagué-Dobón L, Buko Z, Sakhi AK, Casas M, Maitre L, Andrusaityte S, Grazuleviciene R, Gützkow KB, Brantsæter AL, Heude B, Philippat C, Chatzi L, Vafeiadi M, Yang TC, Wright J, Hough A, Ruiz-Arenas C, Nurtdinov RN, Escaramís G, González JR, Thomsen C, Vrijheid M. Common genetic variants associated with urinary phthalate levels in children: A genome-wide study. ENVIRONMENT INTERNATIONAL 2024; 190:108845. [PMID: 38945087 DOI: 10.1016/j.envint.2024.108845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/14/2024] [Accepted: 06/20/2024] [Indexed: 07/02/2024]
Abstract
INTRODUCTION Phthalates, or dieters of phthalic acid, are a ubiquitous type of plasticizer used in a variety of common consumer and industrial products. They act as endocrine disruptors and are associated with increased risk for several diseases. Once in the body, phthalates are metabolized through partially known mechanisms, involving phase I and phase II enzymes. OBJECTIVE In this study we aimed to identify common single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) associated with the metabolism of phthalate compounds in children through genome-wide association studies (GWAS). METHODS The study used data from 1,044 children with European ancestry from the Human Early Life Exposome (HELIX) cohort. Ten phthalate metabolites were assessed in a two-void pooled urine collected at the mean age of 8 years. Six ratios between secondary and primary phthalate metabolites were calculated. Genome-wide genotyping was done with the Infinium Global Screening Array (GSA) and imputation with the Haplotype Reference Consortium (HRC) panel. PennCNV was used to estimate copy number variants (CNVs) and CNVRanger to identify consensus regions. GWAS of SNPs and CNVs were conducted using PLINK and SNPassoc, respectively. Subsequently, functional annotation of suggestive SNPs (p-value < 1E-05) was done with the FUMA web-tool. RESULTS We identified four genome-wide significant (p-value < 5E-08) loci at chromosome (chr) 3 (FECHP1 for oxo-MiNP_oh-MiNP ratio), chr6 (SLC17A1 for MECPP_MEHHP ratio), chr9 (RAPGEF1 for MBzP), and chr10 (CYP2C9 for MECPP_MEHHP ratio). Moreover, 115 additional loci were found at suggestive significance (p-value < 1E-05). Two CNVs located at chr11 (MRGPRX1 for oh-MiNP and SLC35F2 for MEP) were also identified. Functional annotation pointed to genes involved in phase I and phase II detoxification, molecular transfer across membranes, and renal excretion. CONCLUSION Through genome-wide screenings we identified known and novel loci implicated in phthalate metabolism in children. Genes annotated to these loci participate in detoxification, transmembrane transfer, and renal excretion.
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Affiliation(s)
- Mariona Bustamante
- Environment and Health Over the Lifecourse, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
| | | | - Zsanett Buko
- Department of Oncological Science, Huntsman Cancer Institute, Salt Lake City, United States
| | - Amrit Kaur Sakhi
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Maribel Casas
- Environment and Health Over the Lifecourse, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Lea Maitre
- Environment and Health Over the Lifecourse, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Sandra Andrusaityte
- Department of Environmental Science, Vytautas Magnus University, Kaunas, Lithuania
| | | | - Kristine B Gützkow
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Anne-Lise Brantsæter
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Barbara Heude
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004, Paris, France
| | - Claire Philippat
- University Grenoble Alpes, Inserm U-1209, CNRS-UMR-5309, Environmental Epidemiology Applied to Reproduction and Respiratory Health Team, Institute for Advanced Biosciences, 38000, Grenoble, France
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece
| | - Tiffany C Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Amy Hough
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Carlos Ruiz-Arenas
- Computational Biology Program, CIMA University of Navarra, idiSNA, Pamplona 31008, Spain
| | - Ramil N Nurtdinov
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Catalonia, Spain
| | - Geòrgia Escaramís
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Departament de Biomedicina, Institut de Neurociències, Universitat de Barcelona (UB), Barcelona, Spain
| | - Juan R González
- Environment and Health Over the Lifecourse, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Cathrine Thomsen
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Martine Vrijheid
- Environment and Health Over the Lifecourse, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
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Deng WQ, Pigeyre M, Azab SM, Wilson SL, Campbell N, Cawte N, Morrison KM, Atkinson SA, Subbarao P, Turvey SE, Moraes TJ, Mandhane P, Azad MB, Simons E, Pare G, Anand SS. Consistent cord blood DNA methylation signatures of gestational age between South Asian and white European cohorts. Clin Epigenetics 2024; 16:74. [PMID: 38840168 PMCID: PMC11155053 DOI: 10.1186/s13148-024-01684-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 05/23/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Epigenetic modifications, particularly DNA methylation (DNAm) in cord blood, are an important biological marker of how external exposures during gestation can influence the in-utero environment and subsequent offspring development. Despite the recognized importance of DNAm during gestation, comparative studies to determine the consistency of these epigenetic signals across different ethnic groups are largely absent. To address this gap, we first performed epigenome-wide association studies (EWAS) of gestational age (GA) using newborn cord blood DNAm comparatively in a white European (n = 342) and a South Asian (n = 490) birth cohort living in Canada. Then, we capitalized on established cord blood epigenetic GA clocks to examine the associations between maternal exposures, offspring characteristics and epigenetic GA, as well as GA acceleration, defined as the residual difference between epigenetic and chronological GA at birth. RESULTS Individual EWASs confirmed 1,211 and 1,543 differentially methylated CpGs previously reported to be associated with GA, in white European and South Asian cohorts, respectively, with a similar distribution of effects. We confirmed that Bohlin's cord blood GA clock was robustly correlated with GA in white Europeans (r = 0.71; p = 6.0 × 10-54) and South Asians (r = 0.66; p = 6.9 × 10-64). In both cohorts, Bohlin's clock was positively associated with newborn weight and length and negatively associated with parity, newborn female sex, and gestational diabetes. Exclusive to South Asians, the GA clock was positively associated with the newborn ponderal index, while pre-pregnancy weight and gestational weight gain were strongly predictive of increased epigenetic GA in white Europeans. Important predictors of GA acceleration included gestational diabetes mellitus, newborn sex, and parity in both cohorts. CONCLUSIONS These results demonstrate the consistent DNAm signatures of GA and the utility of Bohlin's GA clock across the two populations. Although the overall pattern of DNAm is similar, its connections with the mother's environment and the baby's anthropometrics can differ between the two groups. Further research is needed to understand these unique relationships.
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Affiliation(s)
- Wei Q Deng
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Canada.
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada.
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada.
| | - Marie Pigeyre
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
| | - Sandi M Azab
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Samantha L Wilson
- Department of Obstetrics and Gynecology, McMaster University, Hamilton, Canada
| | - Natalie Campbell
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - Nathan Cawte
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
| | | | | | - Padmaja Subbarao
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
- Hospital for Sick Children, Department of Pediatrics, University of Toronto, Toronto, Canada
- Program in Translational Medicine, SickKids Research Institute, Toronto, Canada
| | - Stuart E Turvey
- Department of Pediatrics, BC Children's Hospital, The University of British Columbia, Vancouver, Canada
| | - Theo J Moraes
- Hospital for Sick Children, Department of Pediatrics, University of Toronto, Toronto, Canada
- Program in Translational Medicine, SickKids Research Institute, Toronto, Canada
| | - Piush Mandhane
- Department of Pediatrics, University of Alberta, Edmonton, Canada
| | - Meghan B Azad
- Department of Pediatrics and Child Health, Children's Hospital Research Institute of Manitoba, University of Manitoba, Winnipeg, Canada
| | - Elinor Simons
- Section of Allergy and Immunology, Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, Canada
| | - Guillaume Pare
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Canada
| | - Sonia S Anand
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada.
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada.
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.
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Downie CG, Highland HM, Alotaibi M, Welch BM, Howard AG, Cheng S, Miller N, Jain M, Kaplan RC, Lilly AG, Long T, Sofer T, Thyagarajan B, Yu B, North KE, Avery CL. Genome-wide association study reveals shared and distinct genetic architecture underlying fatty acid and bioactive oxylipin metabolites in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.21.24307719. [PMID: 38826448 PMCID: PMC11142272 DOI: 10.1101/2024.05.21.24307719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Bioactive fatty acid-derived oxylipin molecules play key roles in mediating inflammation and oxidative stress, which underlie many chronic diseases. Circulating levels of fatty acids and oxylipins are influenced by both environmental and genetic factors; characterizing the genetic architecture of bioactive lipids could yield new insights into underlying biological pathways. Thus, we performed a genome wide association study (GWAS) of n=81 fatty acids and oxylipins in n=11,584 Hispanic Community Health Study/Study of Latinos (HCHS/SOL) participants with genetic and lipidomic data measured at study baseline (58.6% female, mean age = 46.1 years, standard deviation = 13.8 years). Additionally, given the effects of central obesity on inflammation, we examined interactions with waist circumference using two-degree-of-freedom joint tests. Heritability estimates ranged from 0% to 47.9%, and 48 of the 81oxylipins and fatty acids were significantly heritable. Moreover, 40 (49.4%) of the 81 oxylipins and fatty acids had at least one genome-wide significant (p< 6.94E-11) variant resulting in 19 independent genetic loci involved in fatty acid and oxylipin synthesis, as well as downstream pathways. Four loci (lead variant minor allele frequency [MAF] range: 0.08-0.50), including the desaturase-encoding FADS and the OATP1B1 transporter protein-encoding SLCO1B1, exhibited associations with four or more fatty acids and oxylipins. The majority of the 15 remaining loci (87.5%) (lead variant MAF range = 0.03-0.45, mean = 0.23) were only associated with one oxylipin or fatty acid, demonstrating evidence of distinct genetic effects. Finally, while most loci identified in two-degree-of-freedom tests were previously identified in our main effects analyses, we also identified an additional rare variant (MAF = 0.002) near CARS2, a locus previously implicated in inflammation. Our analyses revealed shared and distinct genetic architecture underlying fatty acids and oxylipins, providing insights into genetic factors and motivating future multi-omics work to characterize these compounds and elucidate their roles in disease pathways.
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Affiliation(s)
- Carolina G Downie
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Heather M Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Mona Alotaibi
- Division of Pulmonary, Critical Care and Sleep Medicine, University of California, San Diego, San Diego, CA
| | - Barrett M Welch
- School of Public Health, University of Nevada, Reno, Reno, NV
| | - Annie Green Howard
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - Mohit Jain
- Sapient Bioanalytics, San Diego, CA
- Departments of Medicine and Pharmacology, University of California, San Diego, San Diego, CA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY; Public Health Sciences Division, Fred Hutchison Cancer Center, Seattle, WA
| | - Adam G Lilly
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Tao Long
- Sapient Bioanalytics, San Diego, CA
| | - Tamar Sofer
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical Center, Minneapolis, MN
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
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Luo K, Taryn A, Moon EH, Peters BA, Solomon SD, Daviglus ML, Kansal MM, Thyagarajan B, Gellman MD, Cai J, Burk RD, Knight R, Kaplan RC, Cheng S, Rodriguez CJ, Qi Q, Yu B. Gut microbiota, blood metabolites, and left ventricular diastolic dysfunction in US Hispanics/Latinos. MICROBIOME 2024; 12:85. [PMID: 38725043 PMCID: PMC11084054 DOI: 10.1186/s40168-024-01797-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 03/21/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Left ventricular diastolic dysfunction (LVDD) is an important precursor of heart failure (HF), but little is known about its relationship with gut dysbiosis and microbial-related metabolites. By leveraging the multi-omics data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a study with population at high burden of LVDD, we aimed to characterize gut microbiota associated with LVDD and identify metabolite signatures of gut dysbiosis and incident LVDD. RESULTS We included up to 1996 Hispanic/Latino adults (mean age: 59.4 years; 67.1% female) with comprehensive echocardiography assessments, gut microbiome, and blood metabolome data. LVDD was defined through a composite criterion involving tissue Doppler assessment and left atrial volume index measurements. Among 1996 participants, 916 (45.9%) had prevalent LVDD, and 212 out of 594 participants without LVDD at baseline developed incident LVDD over a median 4.3 years of follow-up. Using multivariable-adjusted analysis of compositions of microbiomes (ANCOM-II) method, we identified 7 out of 512 dominant gut bacterial species (prevalence > 20%) associated with prevalent LVDD (FDR-q < 0.1), with inverse associations being found for Intestinimonas_massiliensis, Clostridium_phoceensis, and Bacteroide_coprocola and positive associations for Gardnerella_vaginali, Acidaminococcus_fermentans, Pseudomonas_aeruginosa, and Necropsobacter_massiliensis. Using multivariable adjusted linear regression, 220 out of 669 circulating metabolites with detection rate > 75% were associated with the identified LVDD-related bacterial species (FDR-q < 0.1), with the majority being linked to Intestinimonas_massiliensis, Clostridium_phoceensis, and Acidaminococcus_fermentans. Furthermore, 46 of these bacteria-associated metabolites, mostly glycerophospholipids, secondary bile acids, and amino acids, were associated with prevalent LVDD (FDR-q < 0.1), 21 of which were associated with incident LVDD (relative risk ranging from 0.81 [p = 0.001, for guanidinoacetate] to 1.25 [p = 9 × 10-5, for 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4)]). The inclusion of these 21 bacterial-related metabolites significantly improved the prediction of incident LVDD compared with a traditional risk factor model (the area under the receiver operating characteristic curve [AUC] = 0.73 vs 0.70, p = 0.001). Metabolite-based proxy association analyses revealed the inverse associations of Intestinimonas_massilliensis and Clostridium_phoceensis and the positive association of Acidaminococcus_fermentans with incident LVDD. CONCLUSION In this study of US Hispanics/Latinos, we identified multiple gut bacteria and related metabolites linked to LVDD, suggesting their potential roles in this preclinical HF entity. Video Abstract.
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Affiliation(s)
- Kai Luo
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Alkis Taryn
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Eun-Hye Moon
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Brandilyn A Peters
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Scott D Solomon
- Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois Chicago College of Medicine, Chicago, IL, 60612, USA
| | - Mayank M Kansal
- Clinical Medicine, University of Illinois College of Medicine, Chicago, IL, 60612, USA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine & Pathology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Marc D Gellman
- Department of Psychology, Clinical Research Building, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA
| | - Jianwen Cai
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Robert D Burk
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Obstetrics and Gynecology and Women's Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Pediatrics, Albert Einstein College of Medicine, NY10461, Bronx, USA
| | - Rob Knight
- Center for Microbiome Innovation, University of California, La Jolla, San Diego, CA, 92093, USA
- Department of Bioengineering, University of California, La Jolla, San Diego, CA, 92093, USA
- Department of Pediatrics, University of California, La Jolla, San Diego, CA, 92093, USA
- Department of Computer Science and Engineering, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Carlos J Rodriguez
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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8
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Brown EA, Kales S, Boyle MJ, Vitti J, Kotliar D, Schaffner S, Tewhey R, Sabeti PC. Three linked variants have opposing regulatory effects on isovaleryl-CoA dehydrogenase gene expression. Hum Mol Genet 2024; 33:270-283. [PMID: 37930192 PMCID: PMC10800014 DOI: 10.1093/hmg/ddad177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/03/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
While genome-wide association studies (GWAS) and positive selection scans identify genomic loci driving human phenotypic diversity, functional validation is required to discover the variant(s) responsible. We dissected the IVD gene locus-which encodes the isovaleryl-CoA dehydrogenase enzyme-implicated by selection statistics, multiple GWAS, and clinical genetics as important to function and fitness. We combined luciferase assays, CRISPR/Cas9 genome-editing, massively parallel reporter assays (MPRA), and a deletion tiling MPRA strategy across regulatory loci. We identified three regulatory variants, including an indel, that may underpin GWAS signals for pulmonary fibrosis and testosterone, and that are linked on a positively selected haplotype in the Japanese population. These regulatory variants exhibit synergistic and opposing effects on IVD expression experimentally. Alleles at these variants lie on a haplotype tagged by the variant most strongly associated with IVD expression and metabolites, but with no functional evidence itself. This work demonstrates how comprehensive functional investigation and multiple technologies are needed to discover the true genetic drivers of phenotypic diversity.
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Affiliation(s)
- Elizabeth A Brown
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Susan Kales
- The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, United States
| | - Michael James Boyle
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
| | - Joseph Vitti
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Dylan Kotliar
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Steve Schaffner
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Ryan Tewhey
- The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, United States
| | - Pardis C Sabeti
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
- Howard Hughes Medical Institute, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
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9
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Li Y, Wong KY, Howard AG, Gordon-Larsen P, Highland HM, Graff M, North KE, Downie CG, Avery CL, Yu B, Young KL, Buchanan VL, Kaplan R, Hou L, Joyce BT, Qi Q, Sofer T, Moon JY, Lin DY. Mendelian randomization with incomplete measurements on the exposure in the Hispanic Community Health Study/Study of Latinos. HGG ADVANCES 2024; 5:100245. [PMID: 37817410 PMCID: PMC10628889 DOI: 10.1016/j.xhgg.2023.100245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 10/02/2023] [Accepted: 10/02/2023] [Indexed: 10/12/2023] Open
Abstract
Mendelian randomization has been widely used to assess the causal effect of a heritable exposure variable on an outcome of interest, using genetic variants as instrumental variables. In practice, data on the exposure variable can be incomplete due to high cost of measurement and technical limits of detection. In this paper, we propose a valid and efficient method to handle both unmeasured and undetectable values of the exposure variable in one-sample Mendelian randomization analysis with individual-level data. We estimate the causal effect of the exposure variable on the outcome using maximum likelihood estimation and develop an expectation maximization algorithm for the computation of the estimator. Simulation studies show that the proposed method performs well in making inference on the causal effect. We apply our method to the Hispanic Community Health Study/Study of Latinos, a community-based prospective cohort study, and estimate the causal effect of several metabolites on phenotypes of interest.
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Affiliation(s)
- Yilun Li
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kin Yau Wong
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
| | - Annie Green Howard
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Penny Gordon-Larsen
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Carolina G Downie
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Christy L Avery
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Kristin L Young
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Victoria L Buchanan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Lifang Hou
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Brian Thomas Joyce
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Dan-Yu Lin
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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10
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Luo K, Chen GC, Zhang Y, Moon JY, Xing J, Peters BA, Usyk M, Wang Z, Hu G, Li J, Selvin E, Rebholz CM, Wang T, Isasi CR, Yu B, Knight R, Boerwinkle E, Burk RD, Kaplan RC, Qi Q. Variant of the lactase LCT gene explains association between milk intake and incident type 2 diabetes. Nat Metab 2024; 6:169-186. [PMID: 38253929 PMCID: PMC11097298 DOI: 10.1038/s42255-023-00961-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 12/04/2023] [Indexed: 01/24/2024]
Abstract
Cow's milk is frequently included in the human diet, but the relationship between milk intake and type 2 diabetes (T2D) remains controversial. Here, using data from the Hispanic Community Health Study/Study of Latinos, we show that in both sexes, higher milk intake is associated with lower risk of T2D in lactase non-persistent (LNP) individuals (determined by a variant of the lactase LCT gene, single nucleotide polymorphism rs4988235 ) but not in lactase persistent individuals. We validate this finding in the UK Biobank. Further analyses reveal that among LNP individuals, higher milk intake is associated with alterations in gut microbiota (for example, enriched Bifidobacterium and reduced Prevotella) and circulating metabolites (for example, increased indolepropionate and reduced branched-chain amino acid metabolites). Many of these metabolites are related to the identified milk-associated bacteria and partially mediate the association between milk intake and T2D in LNP individuals. Our study demonstrates a protective association between milk intake and T2D among LNP individuals and a potential involvement of gut microbiota and blood metabolites in this association.
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Affiliation(s)
- Kai Luo
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Guo-Chong Chen
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Nutrition and Food Hygiene, MOE Key Laboratory of Geriatric Diseases and Immunology, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China
| | - Yanbo Zhang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jiaqian Xing
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Brandilyn A Peters
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Mykhaylo Usyk
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Zheng Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Gang Hu
- Chronic Disease Epidemiology Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Jun Li
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Casey M Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Tao Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Carmen R Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Bing Yu
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Rob Knight
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Robert D Burk
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Obstetrics and Gynecology and Women's Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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11
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Biswas S, Hilser JR, Woodward NC, Wang Z, Gukasyan J, Nemet I, Schwartzman WS, Huang P, Han Y, Fouladian Z, Charugundla S, Spencer NJ, Pan C, Tang WW, Lusis AJ, Hazen SL, Hartiala JA, Allayee H. Effect of Genetic and Dietary Perturbation of Glycine Metabolism on Atherosclerosis in Humans and Mice. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.08.23299748. [PMID: 38168321 PMCID: PMC10760269 DOI: 10.1101/2023.12.08.23299748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Objective Epidemiological and genetic studies have reported inverse associations between circulating glycine levels and risk of coronary artery disease (CAD). However, these findings have not been consistently observed in all studies. We sought to evaluate the causal relationship between circulating glycine levels and atherosclerosis using large-scale genetic analyses in humans and dietary supplementation experiments in mice. Methods Serum glycine levels were evaluated for association with prevalent and incident CAD in the UK Biobank. A multi-ancestry genome-wide association study (GWAS) meta-analysis was carried out to identify genetic determinants for circulating glycine levels, which were then used to evaluate the causal relationship between glycine and risk of CAD by Mendelian randomization (MR). A glycine feeding study was carried out with atherosclerosis-prone apolipoprotein E deficient (ApoE-/-) mice to determine the effects of increased circulating glycine levels on amino acid metabolism, metabolic traits, and aortic lesion formation. Results Among 105,718 subjects from the UK Biobank, elevated serum glycine levels were associated with significantly reduced risk of prevalent CAD (Quintile 5 vs. Quintile 1 OR=0.76, 95% CI 0.67-0.87; P<0.0001) and incident CAD (Quintile 5 vs. Quintile 1 HR=0.70, 95% CI 0.65-0.77; P<0.0001) in models adjusted for age, sex, ethnicity, anti-hypertensive and lipid-lowering medications, blood pressure, kidney function, and diabetes. A meta-analysis of 13 GWAS datasets (total n=230,947) identified 61 loci for circulating glycine levels, of which 26 were novel. MR analyses provided modest evidence that genetically elevated glycine levels were causally associated with reduced systolic blood pressure and risk of type 2 diabetes, but did provide evidence for an association with risk of CAD. Furthermore, glycine-supplementation in ApoE-/- mice did not alter cardiometabolic traits, inflammatory biomarkers, or development of atherosclerotic lesions. Conclusions Circulating glycine levels were inversely associated with risk of prevalent and incident CAD in a large population-based cohort. While substantially expanding the genetic architecture of circulating glycine levels, MR analyses and in vivo feeding studies in humans and mice, respectively, did not provide evidence that the clinical association of this amino acid with CAD represents a causal relationship, despite being associated with two correlated risk factors.
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Affiliation(s)
- Subarna Biswas
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - James R. Hilser
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Nicholas C. Woodward
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Zeneng Wang
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH 44195
| | - Janet Gukasyan
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Ina Nemet
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
| | - William S. Schwartzman
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Pin Huang
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Yi Han
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Zachary Fouladian
- Department of Medicine, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - Sarada Charugundla
- Department of Medicine, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - Neal J. Spencer
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Calvin Pan
- Department of Human Genetics, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - W.H. Wilson Tang
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH 44195
| | - Aldons J. Lusis
- Department of Medicine, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
- Department of Human Genetics, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
- Department of Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - Stanley L. Hazen
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH 44195
| | - Jaana A. Hartiala
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Hooman Allayee
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
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12
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Szrok-Jurga S, Czumaj A, Turyn J, Hebanowska A, Swierczynski J, Sledzinski T, Stelmanska E. The Physiological and Pathological Role of Acyl-CoA Oxidation. Int J Mol Sci 2023; 24:14857. [PMID: 37834305 PMCID: PMC10573383 DOI: 10.3390/ijms241914857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/27/2023] [Accepted: 09/30/2023] [Indexed: 10/15/2023] Open
Abstract
Fatty acid metabolism, including β-oxidation (βOX), plays an important role in human physiology and pathology. βOX is an essential process in the energy metabolism of most human cells. Moreover, βOX is also the source of acetyl-CoA, the substrate for (a) ketone bodies synthesis, (b) cholesterol synthesis, (c) phase II detoxication, (d) protein acetylation, and (d) the synthesis of many other compounds, including N-acetylglutamate-an important regulator of urea synthesis. This review describes the current knowledge on the importance of the mitochondrial and peroxisomal βOX in various organs, including the liver, heart, kidney, lung, gastrointestinal tract, peripheral white blood cells, and other cells. In addition, the diseases associated with a disturbance of fatty acid oxidation (FAO) in the liver, heart, kidney, lung, alimentary tract, and other organs or cells are presented. Special attention was paid to abnormalities of FAO in cancer cells and the diseases caused by mutations in gene-encoding enzymes involved in FAO. Finally, issues related to α- and ω- fatty acid oxidation are discussed.
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Affiliation(s)
- Sylwia Szrok-Jurga
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
| | - Aleksandra Czumaj
- Department of Pharmaceutical Biochemistry, Faculty of Pharmacy, Medical University of Gdansk, 80-211 Gdansk, Poland;
| | - Jacek Turyn
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
| | - Areta Hebanowska
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
| | - Julian Swierczynski
- Institue of Nursing and Medical Rescue, State University of Applied Sciences in Koszalin, 75-582 Koszalin, Poland;
| | - Tomasz Sledzinski
- Department of Pharmaceutical Biochemistry, Faculty of Pharmacy, Medical University of Gdansk, 80-211 Gdansk, Poland;
| | - Ewa Stelmanska
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
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13
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Fuller H, Zhu Y, Nicholas J, Chatelaine HA, Drzymalla EM, Sarvestani AK, Julián-Serrano S, Tahir UA, Sinnott-Armstrong N, Raffield LM, Rahnavard A, Hua X, Shutta KH, Darst BF. Metabolomic epidemiology offers insights into disease aetiology. Nat Metab 2023; 5:1656-1672. [PMID: 37872285 PMCID: PMC11164316 DOI: 10.1038/s42255-023-00903-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/06/2023] [Indexed: 10/25/2023]
Abstract
Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.
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Affiliation(s)
- Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jayna Nicholas
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Haley A Chatelaine
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Emily M Drzymalla
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Afrand K Sarvestani
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | | | - Usman A Tahir
- Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Xinwei Hua
- Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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14
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Moon JY, Chai JC, Yu B, Song RJ, Chen GC, Graff M, Daviglus ML, Chan Q, Thyagarajan B, Castaneda SF, Grove ML, Cai J, Xue X, Mossavar-Rahmani Y, Vasan RS, Boerwinkle E, Kaplan R, Qi Q. Metabolomic Signatures of Sedentary Behavior and Cardiometabolic Traits in US Hispanics/Latinos: Results from HCHS/SOL. Med Sci Sports Exerc 2023; 55:1781-1791. [PMID: 37170952 PMCID: PMC10523950 DOI: 10.1249/mss.0000000000003205] [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] [Indexed: 05/13/2023]
Abstract
PURPOSE The aim of this study was to understand the serum metabolomic signatures of moderate-to-vigorous physical activity (MVPA) and sedentary behavior, and further associate their metabolomic signatures with incident cardiometabolic diseases. METHODS This analysis included 2711 US Hispanics/Latinos from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) aged 18-74 yr (2008-2011). An untargeted, liquid chromatography-mass spectrometry was used to profile the serum metabolome. The associations of metabolites with accelerometer-measured MVPA and sedentary time were examined using survey linear regressions adjusting for covariates. The weighted correlation network analysis identified modules of correlated metabolites in relation to sedentary time, and the modules were associated with incident diabetes, dyslipidemia, and hypertension over the 6-yr follow-up. RESULTS Of 624 metabolites, 5 and 102 were associated with MVPA and sedentary behavior at false discovery rate (FDR) <0.05, respectively, after adjusting for socioeconomic and lifestyle factors. The weighted correlation network analysis identified 8 modules from 102 metabolites associated with sedentary time. Four modules (branched-chain amino acids, erythritol, polyunsaturated fatty acid, creatine) were positively, and the other four (acyl choline, plasmalogen glycerol phosphatidyl choline, plasmalogen glycerol phosphatidyl ethanolamine, urea cycle) were negatively correlated with sedentary time. Among these modules, a higher branched-chain amino acid score and a lower plasmalogen glycerol phosphatidyl choline score were associated with increased risks of diabetes and dyslipidemia. A higher erythritol score was associated with an increased risk of diabetes, and a lower acyl choline score was linked to an increased risk of hypertension. CONCLUSIONS In this study of US Hispanics/Latinos, we identified multiple serum metabolomic signatures of sedentary behavior and their associations with risk of incident diabetes, hypertension, and dyslipidemia. These findings suggest a potential role of circulating metabolites in the links between sedentary behavior and cardiometabolic diseases.
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Affiliation(s)
- Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Jin Choul Chai
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Rebecca J. Song
- Department of Medicine, Boston University School of Medicine, Boston, MA
| | - Guo-chong Chen
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
- Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, CHINA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, NC
| | - Martha L. Daviglus
- Institute for Minority Health Research, University of Illinois Chicago, Chicago, IL
| | - Queenie Chan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, IL
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN
| | | | - Megan L. Grove
- Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, CHINA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC
| | - Xiaonan Xue
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Yasmin Mossavar-Rahmani
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | | | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
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15
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Seeley EH, Liu Z, Yuan S, Stroope C, Cockerham E, Rashdan NA, Delgadillo L, Finney AC, Kumar D, Das S, Razani B, Liu W, Traylor J, Orr AW, Rom O, Pattillo CB, Yurdagul A. Spatially Resolved Metabolites in Stable and Unstable Human Atherosclerotic Plaques Identified by Mass Spectrometry Imaging. Arterioscler Thromb Vasc Biol 2023; 43:1626-1635. [PMID: 37381983 PMCID: PMC10527524 DOI: 10.1161/atvbaha.122.318684] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 06/14/2023] [Indexed: 06/30/2023]
Abstract
BACKGROUND Impairments in carbohydrate, lipid, and amino acid metabolism drive features of plaque instability. However, where these impairments occur within the atheroma remains largely unknown. Therefore, we sought to characterize the spatial distribution of metabolites within stable and unstable atherosclerosis in both the fibrous cap and necrotic core. METHODS Atherosclerotic tissue specimens from 9 unmatched individuals were scored based on the Stary classification scale and subdivided into stable and unstable atheromas. After performing mass spectrometry imaging on these samples, we identified over 850 metabolite-related peaks. Using MetaboScape, METASPACE, and Human Metabolome Database, we confidently annotated 170 of these metabolites and found over 60 of these were different between stable and unstable atheromas. We then integrated these results with an RNA-sequencing data set comparing stable and unstable human atherosclerosis. RESULTS Upon integrating our mass spectrometry imaging results with the RNA-sequencing data set, we discovered that pathways related to lipid metabolism and long-chain fatty acids were enriched in stable plaques, whereas reactive oxygen species, aromatic amino acid, and tryptophan metabolism were increased in unstable plaques. In addition, acylcarnitines and acylglycines were increased in stable plaques whereas tryptophan metabolites were enriched in unstable plaques. Evaluating spatial differences in stable plaques revealed lactic acid in the necrotic core, whereas pyruvic acid was elevated in the fibrous cap. In unstable plaques, 5-hydroxyindoleacetic acid was enriched in the fibrous cap. CONCLUSIONS Our work here represents the first step to defining an atlas of metabolic pathways involved in plaque destabilization in human atherosclerosis. We anticipate this will be a valuable resource and open new avenues of research in cardiovascular disease.
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Affiliation(s)
- Erin H. Seeley
- Department of Chemistry, University of Texas at Austin, TX, USA
| | - Zhipeng Liu
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, IN, USA
| | - Shuai Yuan
- Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, PA, USA
| | - Chad Stroope
- Department of Molecular and Cellular Physiology, LSU Health Sciences Center at Shreveport, LA, USA
| | - Elizabeth Cockerham
- Department of Pathology and Translational Pathobiology, LSU Health Sciences Center at Shreveport, LA, USA
| | - Nabil A Rashdan
- Department of Molecular and Cellular Physiology, LSU Health Sciences Center at Shreveport, LA, USA
| | - Luisa Delgadillo
- Department of Molecular and Cellular Physiology, LSU Health Sciences Center at Shreveport, LA, USA
| | - Alexandra C Finney
- Department of Pathology and Translational Pathobiology, LSU Health Sciences Center at Shreveport, LA, USA
| | - Dhananjay Kumar
- Department of Molecular and Cellular Physiology, LSU Health Sciences Center at Shreveport, LA, USA
| | - Sandeep Das
- Department of Pathology and Translational Pathobiology, LSU Health Sciences Center at Shreveport, LA, USA
| | - Babak Razani
- Cardiovascular Division, Department of Medicine and Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, USA
- John Cochran VA Medical Center, St. Louis, MO, USA
| | - Wanqing Liu
- Department of Pharmaceutical Sciences and Department of Pharmacology, Wayne State University, MI, USA
| | - James Traylor
- Department of Pathology and Translational Pathobiology, LSU Health Sciences Center at Shreveport, LA, USA
| | - A Wayne Orr
- Department of Molecular and Cellular Physiology, LSU Health Sciences Center at Shreveport, LA, USA
- Department of Pathology and Translational Pathobiology, LSU Health Sciences Center at Shreveport, LA, USA
| | - Oren Rom
- Department of Molecular and Cellular Physiology, LSU Health Sciences Center at Shreveport, LA, USA
- Department of Pathology and Translational Pathobiology, LSU Health Sciences Center at Shreveport, LA, USA
| | - Christopher B Pattillo
- Department of Molecular and Cellular Physiology, LSU Health Sciences Center at Shreveport, LA, USA
| | - Arif Yurdagul
- Department of Molecular and Cellular Physiology, LSU Health Sciences Center at Shreveport, LA, USA
- Department of Pathology and Translational Pathobiology, LSU Health Sciences Center at Shreveport, LA, USA
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16
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Abu-Farha M, Joseph S, Mohammad A, Channanath A, Taher I, Al-Mulla F, Mujammami M, Thanaraj TA, Abubaker J, Abdel Rahman AM. Targeted Metabolomics Analysis of Individuals Carrying the ANGPTL8 R59W Variant. Metabolites 2023; 13:972. [PMID: 37755252 PMCID: PMC10536441 DOI: 10.3390/metabo13090972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/15/2023] [Accepted: 08/17/2023] [Indexed: 09/28/2023] Open
Abstract
ANGPTL8 is recognized as a regulator of lipid metabolism through its role in inhibiting lipoprotein lipase activity. ANGPTL8 gene variants, particularly rs2278426 leading to the R59W variant in the protein, have been associated with lipid traits in various ethnicities. We aimed to use metabolomics to understand the impact of the ANGPTL8 R59W variant on metabolites in humans. We used the Biocrates-p400 kit to quantify 408 plasma metabolites in 60 adult male Arab individuals from Kuwait and identify differences in metabolite levels between individuals carrying reference genotypes and those with carrier genotypes at ANGPTL8 rs2278426. Individuals with carrier genotypes (CT+TT) compared to those carrying the reference genotype (CC) showed statistically significant differences in the following metabolites: acylcarnitine (perturbs metabolic pathways), phosphatidylcholine (supports liver function and cholesterol levels), cholesteryl ester (brings chronic inflammatory response to lipoprotein depositions in arteries), α-aminoadipic acid (modulates glucose homeostasis), histamine (regulates glucose/lipid metabolism), sarcosine (links amino acid and lipid metabolism), diacylglycerol 42:1 (regulates homeostasis of cellular lipid stores), and lysophosphatidylcholine (regulates oxidative stress and inflammatory response). Functional aspects attributed to these metabolites indicate that the ANGPTL8 R59W variant influences the concentrations of lipid- and inflammation-related metabolites. This observation further highlights the role of ANGPTL8 in lipid metabolism.
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Affiliation(s)
- Mohamed Abu-Farha
- Department of Biochemistry and Molecular Biology, Dasman Diabetes Institute, Dasman 15462, Kuwait; (M.A.-F.); (S.J.); (A.M.)
| | - Shibu Joseph
- Department of Biochemistry and Molecular Biology, Dasman Diabetes Institute, Dasman 15462, Kuwait; (M.A.-F.); (S.J.); (A.M.)
| | - Anwar Mohammad
- Department of Biochemistry and Molecular Biology, Dasman Diabetes Institute, Dasman 15462, Kuwait; (M.A.-F.); (S.J.); (A.M.)
| | - Arshad Channanath
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Dasman 15462, Kuwait; (A.C.); (F.A.-M.)
| | - Ibrahim Taher
- Microbiology Unit, Department of Pathology, College of Medicine, Jouf University, Sakaka 72388, Saudi Arabia;
| | - Fahd Al-Mulla
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Dasman 15462, Kuwait; (A.C.); (F.A.-M.)
| | - Muhammad Mujammami
- Endocrinology and Diabetes Unit, Department of Medicine, College of Medicine, King Saud University, Riyadh 11421, Saudi Arabia;
- University Diabetes Center, King Saud University Medical City, King Saud University, Riyadh 11421, Saudi Arabia
| | - Thangavel Alphonse Thanaraj
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Dasman 15462, Kuwait; (A.C.); (F.A.-M.)
| | - Jehad Abubaker
- Department of Biochemistry and Molecular Biology, Dasman Diabetes Institute, Dasman 15462, Kuwait; (M.A.-F.); (S.J.); (A.M.)
| | - Anas M. Abdel Rahman
- Metabolomics Section, Department of Clinical Genomics, Centre for Genome Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), Riyadh 11211, Saudi Arabia;
- Department of Biochemistry and Molecular Medicine, College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
- Department of Chemistry, College of Science, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada
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17
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Rodriguez OL, Safonova Y, Silver CA, Shields K, Gibson WS, Kos JT, Tieri D, Ke H, Jackson KJL, Boyd SD, Smith ML, Marasco WA, Watson CT. Genetic variation in the immunoglobulin heavy chain locus shapes the human antibody repertoire. Nat Commun 2023; 14:4419. [PMID: 37479682 PMCID: PMC10362067 DOI: 10.1038/s41467-023-40070-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 07/11/2023] [Indexed: 07/23/2023] Open
Abstract
Variation in the antibody response has been linked to differential outcomes in disease, and suboptimal vaccine and therapeutic responsiveness, the determinants of which have not been fully elucidated. Countering models that presume antibodies are generated largely by stochastic processes, we demonstrate that polymorphisms within the immunoglobulin heavy chain locus (IGH) impact the naive and antigen-experienced antibody repertoire, indicating that genetics predisposes individuals to mount qualitatively and quantitatively different antibody responses. We pair recently developed long-read genomic sequencing methods with antibody repertoire profiling to comprehensively resolve IGH genetic variation, including novel structural variants, single nucleotide variants, and genes and alleles. We show that IGH germline variants determine the presence and frequency of antibody genes in the expressed repertoire, including those enriched in functional elements linked to V(D)J recombination, and overlapping disease-associated variants. These results illuminate the power of leveraging IGH genetics to better understand the regulation, function, and dynamics of the antibody response in disease.
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Affiliation(s)
- Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Yana Safonova
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Catherine A Silver
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Kaitlyn Shields
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - William S Gibson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Justin T Kos
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - David Tieri
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Hanzhong Ke
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Scott D Boyd
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Melissa L Smith
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA.
| | - Wayne A Marasco
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA.
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18
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Han X, Lains I, Li J, Li J, Chen Y, Yu B, Qi Q, Boerwinkle E, Kaplan R, Thyagarajan B, Daviglus M, Joslin CE, Cai J, Guasch-Ferré M, Tobias DK, Rimm E, Ascherio A, Costenbader K, Karlson E, Mucci L, Eliassen AH, Zeleznik O, Miller J, Vavvas DG, Kim IK, Silva R, Miller J, Hu F, Willett W, Lasky-Su J, Kraft P, Richards JB, MacGregor S, Husain D, Liang L. Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration. Cell Rep Med 2023; 4:101085. [PMID: 37348500 PMCID: PMC10394104 DOI: 10.1016/j.xcrm.2023.101085] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/22/2023] [Accepted: 05/22/2023] [Indexed: 06/24/2023]
Abstract
Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk.
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Affiliation(s)
- Xikun Han
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Ines Lains
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jinglun Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yiheng Chen
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical Center, Minneapolis, MN, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Charlotte E Joslin
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Eric Rimm
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alberto Ascherio
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karen Costenbader
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Elizabeth Karlson
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Lorelei Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Miller
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Demetrios G Vavvas
- Retina Service, Ines and Fredrick Yeatts Retinal Research Laboratory, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Ivana K Kim
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Rufino Silva
- Ophthalmology Unit, Centro Hospitalar e Universitário de Coimbra (CHUC), Coimbra, Portugal; University Clinic of Ophthalmology, Faculty of Medicine, University of Coimbra (FMUC), Coimbra, Portugal; Clinical Academic Center of Coimbra (CACC), Coimbra, Portugal
| | - Joan Miller
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Frank Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Walter Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jessica Lasky-Su
- Systems Genetics and Genomics Unit, Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - J Brent Richards
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montréal, QC, Canada; Department of Twin Research, King's College London, London, UK; Five Prime Sciences Inc, Montréal, QC, Canada
| | - Stuart MacGregor
- Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Deeba Husain
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA.
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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19
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Reynolds KM, Horimoto ARVR, Lin BM, Zhang Y, Kurniansyah N, Yu B, Boerwinkle E, Qi Q, Kaplan R, Daviglus M, Hou L, Zhou LY, Cai J, Shaikh SR, Sofer T, Browning SR, Franceschini N. Ancestry-driven metabolite variation provides insights into disease states in admixed populations. Genome Med 2023; 15:52. [PMID: 37461045 PMCID: PMC10351197 DOI: 10.1186/s13073-023-01209-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 07/10/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Metabolic pathways are related to physiological functions and disease states and are influenced by genetic variation and environmental factors. Hispanics/Latino individuals have ancestry-derived genomic regions (local ancestry) from their recent admixture that have been less characterized for associations with metabolite abundance and disease risk. METHODS We performed admixture mapping of 640 circulating metabolites in 3887 Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Metabolites were quantified in fasting serum through non-targeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Replication was performed in 1856 nonoverlapping HCHS/SOL participants with metabolomic data. RESULTS By leveraging local ancestry, this study identified significant ancestry-enriched associations for 78 circulating metabolites at 484 independent regions, including 116 novel metabolite-genomic region associations that replicated in an independent sample. Among the main findings, we identified Native American enriched genomic regions at chromosomes 11 and 15, mapping to FADS1/FADS2 and LIPC, respectively, associated with reduced long-chain polyunsaturated fatty acid metabolites implicated in metabolic and inflammatory pathways. An African-derived genomic region at chromosome 2 was associated with N-acetylated amino acid metabolites. This region, mapped to ALMS1, is associated with chronic kidney disease, a disease that disproportionately burdens individuals of African descent. CONCLUSIONS Our findings provide important insights into differences in metabolite quantities related to ancestry in admixed populations including metabolites related to regulation of lipid polyunsaturated fatty acids and N-acetylated amino acids, which may have implications for common diseases in populations.
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Affiliation(s)
- Kaylia M Reynolds
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Epidemiology, University of North Carolina, 123 W Franklin St, Suite 401, NC, NC 27516, Chapel Hill, USA
| | | | - Bridget M Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Ying Zhang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Bing Yu
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Laura Y Zhou
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Saame Raza Shaikh
- Department of Nutrition, University of North Carolina, Chapel Hill, NC, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Departments of Medicine and Biostatistics, Harvard University, Boston, MA, USA
| | - Sharon R Browning
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, 123 W Franklin St, Suite 401, NC, NC 27516, Chapel Hill, USA.
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20
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Schlosser P, Scherer N, Grundner-Culemann F, Monteiro-Martins S, Haug S, Steinbrenner I, Uluvar B, Wuttke M, Cheng Y, Ekici AB, Gyimesi G, Karoly ED, Kotsis F, Mielke J, Gomez MF, Yu B, Grams ME, Coresh J, Boerwinkle E, Köttgen M, Kronenberg F, Meiselbach H, Mohney RP, Akilesh S, Schmidts M, Hediger MA, Schultheiss UT, Eckardt KU, Oefner PJ, Sekula P, Li Y, Köttgen A. Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine. Nat Genet 2023:10.1038/s41588-023-01409-8. [PMID: 37277652 DOI: 10.1038/s41588-023-01409-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 04/26/2023] [Indexed: 06/07/2023]
Abstract
The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments.
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Affiliation(s)
- Pascal Schlosser
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Nora Scherer
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine, University of Freiburg, Freiburg, Germany
| | - Franziska Grundner-Culemann
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Sara Monteiro-Martins
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Stefan Haug
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Inga Steinbrenner
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Burulça Uluvar
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Yurong Cheng
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Arif B Ekici
- Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Gergely Gyimesi
- Membrane Transport Discovery Lab, Department of Nephrology and Hypertension and Department of Biomedical Research, University of Bern, Bern, Switzerland
| | | | - Fruzsina Kotsis
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Department of Medicine IV-Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Johanna Mielke
- Research and Early Development, Pharmaceuticals Division, Bayer AG, Wuppertal, Germany
| | - Maria F Gomez
- Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund University, Lund, Sweden
| | - Bing Yu
- Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Morgan E Grams
- New York University Grossman School of Medicine, New York, NY, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Eric Boerwinkle
- Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Michael Köttgen
- Department of Medicine IV-Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signalling Studies (CIBSS), Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - Heike Meiselbach
- Department of Nephrology and Hypertension, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | - Shreeram Akilesh
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Miriam Schmidts
- Centre for Integrative Biological Signalling Studies (CIBSS), Albert-Ludwigs-University Freiburg, Freiburg, Germany
- Freiburg University Faculty of Medicine, Center for Pediatrics and Adolescent Medicine, University Hospital Freiburg, Freiburg, Germany
| | - Matthias A Hediger
- Membrane Transport Discovery Lab, Department of Nephrology and Hypertension and Department of Biomedical Research, University of Bern, Bern, Switzerland
| | - Ulla T Schultheiss
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Department of Medicine IV-Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Kai-Uwe Eckardt
- Department of Nephrology and Hypertension, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Peter J Oefner
- Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Peggy Sekula
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Yong Li
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Centre for Integrative Biological Signalling Studies (CIBSS), Albert-Ludwigs-University Freiburg, Freiburg, Germany.
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21
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Feofanova EV, Brown MR, Alkis T, Manuel AM, Li X, Tahir UA, Li Z, Mendez KM, Kelly RS, Qi Q, Chen H, Larson MG, Lemaitre RN, Morrison AC, Grieser C, Wong KE, Gerszten RE, Zhao Z, Lasky-Su J, Yu B. Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations. Nat Commun 2023; 14:3111. [PMID: 37253714 PMCID: PMC10229598 DOI: 10.1038/s41467-023-38800-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/16/2023] [Indexed: 06/01/2023] Open
Abstract
Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.
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Affiliation(s)
- Elena V Feofanova
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
| | - Michael R Brown
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
| | - Taryn Alkis
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
| | - Astrid M Manuel
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kevin M Mendez
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Retina Service, Massachusetts Eye and Ear, Harvard Medical School, 243 Charles Street, Boston, MA, USA
| | - Rachel S Kelly
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Han Chen
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Martin G Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Alanna C Morrison
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
| | | | | | - Robert E Gerszten
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Zhongming Zhao
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Bing Yu
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA.
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22
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Granot-Hershkovitz E, Spitzer B, Yang Y, Tarraf W, Yu B, Boerwinkle E, Fornage M, Mosley TH, DeCarli C, Kristal BS, González HM, Sofer T. Genetic loci of beta-aminoisobutyric acid are associated with aging-related mild cognitive impairment. Transl Psychiatry 2023; 13:140. [PMID: 37120436 PMCID: PMC10148805 DOI: 10.1038/s41398-023-02437-y] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 04/14/2023] [Accepted: 04/19/2023] [Indexed: 05/01/2023] Open
Abstract
We studied the genetic associations of a previously developed Metabolomic Risk Score (MRS) for Mild Cognitive Impairment (MCI) and beta-aminoisobutyric acid metabolite (BAIBA)-the metabolite highlighted by results from a genome-wide association study (GWAS) of the MCI-MRS, and assessed their association with MCI in datasets of diverse race/ethnicities. We first performed a GWAS for the MCI-MRS and BAIBA, in Hispanic/Latino adults (n = 3890) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We identified ten independent genome-wide significant (p value <5 × 10-8) variants associated with MCI-MRS or BAIBA. Variants associated with the MCI-MRS are located in the Alanine-Glyoxylate Aminotransferase 2 (AGXT2 gene), which is known to be associated with BAIBA metabolism. Variants associated with BAIBA are located in the AGXT2 gene and in the SLC6A13 gene. Next, we tested the variants' association with MCI in independent datasets of n = 3178 HCHS/SOL older individuals, n = 3775 European Americans, and n = 1032 African Americans from the Atherosclerosis Risk In Communities (ARIC) study. Variants were considered associated with MCI if their p value <0.05 in the meta-analysis of the three datasets and their direction of association was consistent with expectation. Rs16899972 and rs37369 from the AGXT2 region were associated with MCI. Mediation analysis supported the mediation effect of BAIBA between the two genetic variants and MCI (p value = 0.004 for causal mediated effect). In summary, genetic variants in the AGXT2 region are associated with MCI in Hispanic/Latino, African, and European American populations in the USA, and their effect is likely mediated by changes in BAIBA levels.
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Affiliation(s)
- Einat Granot-Hershkovitz
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Brian Spitzer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Yunju Yang
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Wassim Tarraf
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | - Bing Yu
- Human Genetics Center, School of Public Health University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Eric Boerwinkle
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Thomas H Mosley
- Department of Neurology, School of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Charles DeCarli
- Alzheimer's Disease Center, Department of Neurology, University of California, Davis, Sacramento, CA, USA
| | - Bruce S Kristal
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Hector M González
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H Chan School of Public Health, Boston, MA, USA.
- Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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23
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Hong KU, Walls KM, Hein DW. Non-coding and intergenic genetic variants of human arylamine N-acetyltransferase 2 (NAT2) gene are associated with differential plasma lipid and cholesterol levels and cardiometabolic disorders. Front Pharmacol 2023; 14:1091976. [PMID: 37077812 PMCID: PMC10106703 DOI: 10.3389/fphar.2023.1091976] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 03/02/2023] [Indexed: 04/05/2023] Open
Abstract
Arylamine N-acetyltransferase 2 (NAT2) is a phase II metabolic enzyme, best known for metabolism of aromatic amines and hydrazines. Genetic variants occurring in the NAT2 coding region have been well-defined and are known to affect the enzyme activity or protein stability. Individuals can be categorized into rapid, intermediate, and slow acetylator phenotypes that significantly alter their ability to metabolize arylamines, including drugs (e.g., isoniazid) and carcinogens (e.g., 4-aminobiphenyl). However, functional studies on non-coding or intergenic variants of NAT2 are lacking. Multiple, independent genome wide association studies (GWAS) have reported that non-coding or intergenic variants of NAT2 are associated with elevated plasma lipid and cholesterol levels, as well as cardiometabolic disorders, suggesting a novel cellular role of NAT2 in lipid and cholesterol homeostasis. The current review highlights and summarizes GWAS reports that are relevant to this association. We also present a new finding that seven, non-coding, intergenic NAT2 variants (i.e., rs4921913, rs4921914, rs4921915, rs146812806, rs35246381, rs35570672, and rs1495741), which have been associated with plasma lipid and cholesterol levels, are in linkage disequilibrium with one another, and thus form a novel haplotype. The dyslipidemia risk alleles of non-coding NAT2 variants are associated with rapid NAT2 acetylator phenotype, suggesting that differential systemic NAT2 activity might be a risk factor for developing dyslipidemia. The current review also discusses the findings of recent reports that are supportive of the role of NAT2 in lipid or cholesterol synthesis and transport. In summary, we review data suggesting that human NAT2 is a novel genetic factor that influences plasma lipid and cholesterol levels and alters the risk of cardiometabolic disorders. The proposed novel role of NAT2 merits further investigations.
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24
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Granot-Hershkovitz E, He S, Bressler J, Yu B, Tarraf W, Rebholz CM, Cai J, Chan Q, Garcia TP, Mosley T, Kristal BS, DeCarli C, Fornage M, Chen GC, Qi Q, Kaplan R, Gonzalez HM, Sofer T. Plasma metabolites associated with cognitive function across race/ethnicities affirming the importance of healthy nutrition. Alzheimers Dement 2023; 19:1331-1342. [PMID: 36111689 PMCID: PMC10017373 DOI: 10.1002/alz.12786] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/08/2022] [Accepted: 07/22/2022] [Indexed: 11/05/2022]
Abstract
INTRODUCTION We studied the replication and generalization of previously identified metabolites potentially associated with global cognitive function in multiple race/ethnicities and assessed the contribution of diet to these associations. METHODS We tested metabolite-cognitive function associations in U.S.A. Hispanic/Latino adults (n = 2222) from the Community Health Study/ Study of Latinos (HCHS/SOL) and in European (n = 1365) and African (n = 478) Americans from the Atherosclerosis Risk In Communities (ARIC) Study. We applied Mendelian Randomization (MR) analyses to assess causal associations between the metabolites and cognitive function and between Mediterranean diet and cognitive function. RESULTS Six metabolites were consistently associated with lower global cognitive function across all studies. Of these, four were sugar-related (e.g., ribitol). MR analyses provided weak evidence for a potential causal effect of ribitol on cognitive function and bi-directional effects of cognitive performance on diet. DISCUSSION Several diet-related metabolites were associated with global cognitive function across studies with different race/ethnicities. HIGHLIGHTS Metabolites associated with cognitive function in Puerto Rican adults were recently identified. We demonstrate the generalizability of these associations across diverse race/ethnicities. Most identified metabolites are related to sugars. Mendelian Randomization (MR) provides weak evidence for a causal effect of ribitol on cognitive function. Beta-cryptoxanthin and other metabolites highlight the importance of a healthy diet.
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Affiliation(s)
- Einat Granot-Hershkovitz
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Shan He
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jan Bressler
- Human Genetics Center, School of Public Health University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bing Yu
- Human Genetics Center, School of Public Health University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Wassim Tarraf
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | - Casey M. Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jianwen Cai
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, CA, USA
| | - Queenie Chan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Tanya P. Garcia
- Department of Neurology, School of medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Thomas Mosley
- Department of Neurology, School of medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bruce S. Kristal
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Charles DeCarli
- Alzheimer’s Disease Center, Department of Neurology, University of California, Davis, Sacramento, CA, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Guo-Chong Chen
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, China
| | - Qibin Qi
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle WA, USA
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle WA, USA
| | - Hector M. Gonzalez
- Department of Neurosciences and Shiley-Marcos Alzheimer’s Disease Center, University of California, San Diego, La Jolla, CA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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25
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Yang G, Mishra M, Perera MA. Multi-Omics Studies in Historically Excluded Populations: The Road to Equity. Clin Pharmacol Ther 2023; 113:541-556. [PMID: 36495075 DOI: 10.1002/cpt.2818] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022]
Abstract
Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.
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Affiliation(s)
- Guang Yang
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Mrinal Mishra
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Minoli A Perera
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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26
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Elsea SH, Posey JE. Metabolic individuality: Limitations, challenges, and potential for clinical utility. Cell Metab 2023; 35:233-235. [PMID: 36754017 DOI: 10.1016/j.cmet.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
In Nature Medicine, Surendran and colleagues recently reported the analysis of human plasma metabolomic data for 913 metabolites in ∼20,000 individuals, identifying 2,599 metabolite-genetic variant associations and >400 metabolite signatures comprised of jointly regulated metabolites. This extensive atlas of variant-metabolite relationships reveals novel genomic mechanisms driving metabolic phenotypes.
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Affiliation(s)
- Sarah H Elsea
- Department of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA.
| | - Jennifer E Posey
- Department of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA.
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27
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Kurniansyah N, Wallace DA, Zhang Y, Yu B, Cade B, Wang H, Ochs-Balcom HM, Reiner AP, Ramos AR, Smith JD, Cai J, Daviglus M, Zee PC, Kaplan R, Kooperberg C, Rich SS, Rotter JI, Gharib SA, Redline S, Sofer T. An integrated multi-omics analysis of sleep-disordered breathing traits implicates P2XR4 purinergic signaling. Commun Biol 2023; 6:125. [PMID: 36721044 PMCID: PMC9889381 DOI: 10.1038/s42003-023-04520-y] [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: 08/24/2022] [Accepted: 01/23/2023] [Indexed: 02/01/2023] Open
Abstract
Sleep Disordered Breathing (SDB) is a common disease associated with increased risk for cardiometabolic, cardiovascular, and cognitive diseases. How SDB affects the molecular environment is still poorly understood. We study the association of three SDB measures with gene expression measured using RNA-seq in multiple blood tissues from the Multi-Ethnic Study of Atherosclerosis. We develop genetic instrumental variables for the associated transcripts as polygenic risk scores (tPRS), then generalize and validate the tPRS in the Women's Health Initiative. We measure the associations of the validated tPRS with SDB and serum metabolites in Hispanic Community Health Study/Study of Latinos. Here we find differential gene expression by blood cell type in relation to SDB traits and link P2XR4 expression to average oxyhemoglobin saturation during sleep and butyrylcarnitine (C4) levels. These findings can be used to develop interventions to alleviate the effect of SDB on the human molecular environment.
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Affiliation(s)
- Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Danielle A Wallace
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Ying Zhang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Brian Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Heather M Ochs-Balcom
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Alexander P Reiner
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Alberto R Ramos
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Joshua D Smith
- Northwest Genomic Center, University of Washington, Seattle, WA, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina, at Chapel Hill, NC, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Phyllis C Zee
- Division of Sleep Medicine, Department of Neurology, Northwestern University, Chicago, IL, USA
| | - Robert Kaplan
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology & Population Health, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Sina A Gharib
- Computational Medicine Core, Center for Lung Biology, UW Medicine Sleep Center, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
- Departments of Medicine and of Biostatistics, Harvard University, Boston, MA, USA.
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28
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Iwasaki T, Kamatani Y, Sonomura K, Kawaguchi S, Kawaguchi T, Takahashi M, Ohmura K, Sato TA, Matsuda F. Genetic influences on human blood metabolites in the Japanese population. iScience 2023; 26:105738. [PMID: 36582826 PMCID: PMC9792902 DOI: 10.1016/j.isci.2022.105738] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 10/08/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
An increase in ethnic diversity in genetic studies has the potential to provide unprecedented insights into how genetic variations influence human phenotypes. In this study, we conducted a quantitative trait locus (QTL) analysis of 121 metabolites measured using gas chromatography-mass spectrometry with plasma samples from 4,888 Japanese individuals. We found 60 metabolite-gene associations, of which 13 have not been previously reported. Meta-analyses with another Japanese and a European study identified six and two additional unreported loci, respectively. Genetic variants influencing metabolite levels were more enriched in protein-coding regions than in the regulatory regions while being associated with the risk of various diseases. Finally, we identified a signature of strong negative selection for uric acid ( S ˆ = -1.53, p = 6.2 × 10-18). Our study expanded the knowledge of genetic influences on human blood metabolites, providing valuable insights into their physiological, pathological, and selective properties.
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Affiliation(s)
- Takeshi Iwasaki
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan.,Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Yoichiro Kamatani
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Kazuhiro Sonomura
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan.,Life Science Research Center, Shimadzu Corporation, Kyoto 604-8511, Japan
| | - Shuji Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Meiko Takahashi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Taka-Aki Sato
- Life Science Research Center, Shimadzu Corporation, Kyoto 604-8511, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
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29
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Chen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, Cerani A, Liang KYH, Yoshiji S, Willett JDS, Su CY, Raina P, Greenwood CMT, Farjoun Y, Forgetta V, Langenberg C, Zhou S, Ohlsson C, Richards JB. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet 2023; 55:44-53. [PMID: 36635386 PMCID: PMC7614162 DOI: 10.1038/s41588-022-01270-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 87.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/18/2022] [Indexed: 01/14/2023]
Abstract
Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.
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Affiliation(s)
- Yiheng Chen
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | | | - Isobel D Stewart
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Guillaume Butler-Laporte
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Tomoko Nakanishi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Agustin Cerani
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Kevin Y H Liang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Satoshi Yoshiji
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Julian Daniel Sunday Willett
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- McGill Genome Centre, McGill University, Montreal, Quebec, Canada
| | - Chen-Yang Su
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Computer Science, McGill University, Montreal, Quebec, Canada
| | - Parminder Raina
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada
| | - Yossi Farjoun
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Fulcrum Genomics, Boulder, CO, USA
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sirui Zhou
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Claes Ohlsson
- Sahlgrenska Osteoporosis Centre, Centre of Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
- 5 Prime Sciences Inc, Montreal, Quebec, Canada.
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Twin Research, King's College London, London, UK.
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30
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Yin X, Bose D, Kwon A, Hanks SC, Jackson AU, Stringham HM, Welch R, Oravilahti A, Fernandes Silva L, Locke AE, Fuchsberger C, Service SK, Erdos MR, Bonnycastle LL, Kuusisto J, Stitziel NO, Hall IM, Morrison J, Ripatti S, Palotie A, Freimer NB, Collins FS, Mohlke KL, Scott LJ, Fauman EB, Burant C, Boehnke M, Laakso M, Wen X. Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk. Am J Hum Genet 2022; 109:1727-1741. [PMID: 36055244 PMCID: PMC9606383 DOI: 10.1016/j.ajhg.2022.08.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/09/2022] [Indexed: 01/25/2023] Open
Abstract
Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.
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Affiliation(s)
- Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Debraj Bose
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Annie Kwon
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Sarah C Hanks
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Anniina Oravilahti
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Institute for Biomedicine, Eurac Research, Bolzano 39100, Italy
| | - Susan K Service
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Michael R Erdos
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lori L Bonnycastle
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland; Center for Medicine and Clinical Research, Kuopio University Hospital, Kuopio 70210, Finland
| | - Nathan O Stitziel
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA; Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO 63110, USA; Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Ira M Hall
- Center for Genomic Health, Department of Genetics, Yale University, New Haven, CT 06510, USA
| | - Jean Morrison
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki 00290, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki 00290, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology, and Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nelson B Freimer
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Francis S Collins
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - Charles Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland.
| | - Xiaoquan Wen
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
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31
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Liu C, Wang Z, Hui Q, Chiang Y, Chen J, Brijkumar J, Edwards JA, Ordonez CE, Dudgeon MR, Sunpath H, Pillay S, Moodley P, Kuritzkes DR, Moosa MYS, Jones DP, Marconi VC, Sun YV. Crosstalk between Host Genome and Metabolome among People with HIV in South Africa. Metabolites 2022; 12:metabo12070624. [PMID: 35888748 PMCID: PMC9316179 DOI: 10.3390/metabo12070624] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/16/2022] [Accepted: 07/01/2022] [Indexed: 02/01/2023] Open
Abstract
Genome-wide association studies (GWAS) of circulating metabolites have revealed the role of genetic regulation on the human metabolome. Most previous investigations focused on European ancestry, and few studies have been conducted among populations of African descent living in Africa, where the infectious disease burden is high (e.g., human immunodeficiency virus (HIV)). It is important to understand the genetic associations of the metabolome in diverse at-risk populations including people with HIV (PWH) living in Africa. After a thorough literature review, the reported significant gene−metabolite associations were tested among 490 PWH in South Africa. Linear regression was used to test associations between the candidate metabolites and genetic variants. GWAS of 154 plasma metabolites were performed to identify novel genetic associations. Among the 29 gene−metabolite associations identified in the literature, we replicated 10 in South Africans with HIV. The UGT1A cluster was associated with plasma levels of biliverdin and bilirubin; SLC16A9 and CPS1 were associated with carnitine and creatine, respectively. We also identified 22 genetic associations with metabolites using a genome-wide significance threshold (p-value < 5 × 10−8). In a GWAS of plasma metabolites in South African PWH, we replicated reported genetic associations across ancestries, and identified novel genetic associations using a metabolomics approach.
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Affiliation(s)
- Chang Liu
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA; (C.L.); (Q.H.); (Y.C.); (J.C.)
| | - Zicheng Wang
- College of Arts and Sciences, Emory University, Atlanta, GA 30322, USA;
| | - Qin Hui
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA; (C.L.); (Q.H.); (Y.C.); (J.C.)
| | - Yiyun Chiang
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA; (C.L.); (Q.H.); (Y.C.); (J.C.)
| | - Junyu Chen
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA; (C.L.); (Q.H.); (Y.C.); (J.C.)
| | - Jaysingh Brijkumar
- Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban 4041, South Africa; (J.B.); (H.S.); (S.P.); (M.Y.S.M.)
| | - Johnathan A. Edwards
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA;
- School of Medicine, Emory University, Atlanta, GA 30322, USA; (M.R.D.); (D.P.J.); (V.C.M.)
- Lincoln International Institute for Rural Health, School of Health and Social Care, University of Lincoln, Lincoln LN6 7TS, UK
| | - Claudia E. Ordonez
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA;
| | - Mathew R. Dudgeon
- School of Medicine, Emory University, Atlanta, GA 30322, USA; (M.R.D.); (D.P.J.); (V.C.M.)
| | - Henry Sunpath
- Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban 4041, South Africa; (J.B.); (H.S.); (S.P.); (M.Y.S.M.)
| | - Selvan Pillay
- Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban 4041, South Africa; (J.B.); (H.S.); (S.P.); (M.Y.S.M.)
| | - Pravi Moodley
- National Health Laboratory Service, School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban 4011, South Africa;
| | - Daniel R. Kuritzkes
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Mohamed Y. S. Moosa
- Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban 4041, South Africa; (J.B.); (H.S.); (S.P.); (M.Y.S.M.)
| | - Dean P. Jones
- School of Medicine, Emory University, Atlanta, GA 30322, USA; (M.R.D.); (D.P.J.); (V.C.M.)
| | - Vincent C. Marconi
- School of Medicine, Emory University, Atlanta, GA 30322, USA; (M.R.D.); (D.P.J.); (V.C.M.)
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA;
- Emory Vaccine Center, Atlanta, GA 30322, USA
| | - Yan V. Sun
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA; (C.L.); (Q.H.); (Y.C.); (J.C.)
- Correspondence: ; Tel.: +1-404-727-9090
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32
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Vasishta S, Ganesh K, Umakanth S, Joshi MB. Ethnic disparities attributed to the manifestation in and response to type 2 diabetes: insights from metabolomics. Metabolomics 2022; 18:45. [PMID: 35763080 PMCID: PMC9239976 DOI: 10.1007/s11306-022-01905-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 04/13/2022] [Indexed: 11/21/2022]
Abstract
Type 2 diabetes (T2D) associated health disparities among different ethnicities have long been known. Ethnic variations also exist in T2D related comorbidities including insulin resistance, vascular complications and drug response. Genetic heterogeneity, dietary patterns, nutrient metabolism and gut microbiome composition attribute to ethnic disparities in both manifestation and progression of T2D. These factors differentially regulate the rate of metabolism and metabolic health. Metabolomics studies have indicated significant differences in carbohydrate, lipid and amino acid metabolism among ethnicities. Interestingly, genetic variations regulating lipid and amino acid metabolism might also contribute to inter-ethnic differences in T2D. Comprehensive and comparative metabolomics analysis between ethnicities might help to design personalized dietary regimen and newer therapeutic strategies. In the present review, we explore population based metabolomics data to identify inter-ethnic differences in metabolites and discuss how (a) genetic variations, (b) dietary patterns and (c) microbiome composition may attribute for such differences in T2D.
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Affiliation(s)
- Sampara Vasishta
- Department of Ageing Research, Manipal School of Life Sciences, Manipal Academy of Higher Education, 576104, Manipal, India
| | - Kailash Ganesh
- Department of Ageing Research, Manipal School of Life Sciences, Manipal Academy of Higher Education, 576104, Manipal, India
| | | | - Manjunath B Joshi
- Department of Ageing Research, Manipal School of Life Sciences, Manipal Academy of Higher Education, 576104, Manipal, India.
- Manipal School of Life Sciences, Planetarium Complex Manipal Academy of Higher Education Manipal, 576104, Manipal, India.
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33
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Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies. Metabolites 2022; 12:metabo12060512. [PMID: 35736446 PMCID: PMC9229972 DOI: 10.3390/metabo12060512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/27/2022] [Accepted: 05/27/2022] [Indexed: 02/04/2023] Open
Abstract
The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal–offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant–metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal–offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal–offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.
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34
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Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children. Metabolites 2022; 12:metabo12060474. [PMID: 35736407 PMCID: PMC9228478 DOI: 10.3390/metabo12060474] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 02/01/2023] Open
Abstract
Variation in metabolite levels reflects individual differences in genetic and environmental factors. Here, we investigated the role of these factors in urinary metabolomics data in children. We examined the effects of sex and age on 86 metabolites, as measured on three metabolomics platforms that target amines, organic acids, and steroid hormones. Next, we estimated their heritability in a twin cohort of 1300 twins (age range: 5.7–12.9 years). We observed associations between age and 50 metabolites and between sex and 21 metabolites. The monozygotic (MZ) and dizygotic (DZ) correlations for the urinary metabolites indicated a role for non-additive genetic factors for 50 amines, 13 organic acids, and 6 steroids. The average broad-sense heritability for these amines, organic acids, and steroids was 0.49 (range: 0.25–0.64), 0.50 (range: 0.33–0.62), and 0.64 (range: 0.43–0.81), respectively. For 6 amines, 7 organic acids, and 4 steroids the twin correlations indicated a role for shared environmental factors and the average narrow-sense heritability was 0.50 (range: 0.37–0.68), 0.50 (range; 0.23–0.61), and 0.47 (range: 0.32–0.70) for these amines, organic acids, and steroids. We conclude that urinary metabolites in children have substantial heritability, with similar estimates for amines and organic acids, and higher estimates for steroid hormones.
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35
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Avery CL, Howard AG, Ballou AF, Buchanan VL, Collins JM, Downie CG, Engel SM, Graff M, Highland HM, Lee MP, Lilly AG, Lu K, Rager JE, Staley BS, North KE, Gordon-Larsen P. Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:55001. [PMID: 35533073 PMCID: PMC9084332 DOI: 10.1289/ehp9098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.
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Affiliation(s)
- Christy L Avery
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Annie Green Howard
- Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Anna F Ballou
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Victoria L Buchanan
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jason M Collins
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Carolina G Downie
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Stephanie M Engel
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Moa P Lee
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Adam G Lilly
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Sociology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kun Lu
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Julia E Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Brooke S Staley
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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36
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Lee Y, Chen H, Chen W, Qi Q, Afshar M, Cai J, Daviglus ML, Thyagarajan B, North KE, London SJ, Boerwinkle E, Celedón JC, Kaplan RC, Yu B. Metabolomic Associations of Asthma in the Hispanic Community Health Study/Study of Latinos. Metabolites 2022; 12:metabo12040359. [PMID: 35448546 PMCID: PMC9028429 DOI: 10.3390/metabo12040359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/02/2022] [Accepted: 04/12/2022] [Indexed: 12/17/2022] Open
Abstract
Asthma disproportionally affects Hispanic and/or Latino backgrounds; however, the relation between circulating metabolites and asthma remains unclear. We conducted a cross-sectional study associating 640 individual serum metabolites, as well as twelve metabolite modules, with asthma in 3347 Hispanic/Latino background participants (514 asthmatics, 15.36%) from the Hispanic/Latino Community Health Study/Study of Latinos. Using survey logistic regression, per standard deviation (SD) increase in 1-arachidonoyl-GPA (20:4) was significantly associated with 32% high odds of asthma after accounting for clinical risk factors (p = 6.27 × 10−5), and per SD of the green module, constructed using weighted gene co-expression network, was suggestively associated with 25% high odds of asthma (p = 0.006). In the stratified analyses by sex and Hispanic and/or Latino backgrounds, the effect of 1-arachidonoyl-GPA (20:4) and the green module was predominantly observed in women (OR = 1.24 and 1.37, p < 0.001) and people of Cuban and Puerto-Rican backgrounds (OR = 1.25 and 1.27, p < 0.01). Mutations in Fatty Acid Desaturase 2 (FADS2) affected the levels of 1-arachidonoyl-GPA (20:4), and Mendelian Randomization analyses revealed that high genetically regulated 1-arachidonoyl-GPA (20:4) levels were associated with increased odds of asthma (p < 0.001). The findings reinforce a molecular basis for asthma etiology, and the potential causal effect of 1-arachidonoyl-GPA (20:4) on asthma provides an opportunity for future intervention.
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Affiliation(s)
- Yura Lee
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (Y.L.); (H.C.); (E.B.)
| | - Han Chen
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (Y.L.); (H.C.); (E.B.)
| | - Wei Chen
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15224, USA; (W.C.); (J.C.C.)
| | - Qibin Qi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA;
| | - Majid Afshar
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA; (M.A.); (R.C.K.)
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA;
| | - Martha L. Daviglus
- Institute of Minority Health Research, University of Illinois College of Medicine, Chicago, IL 60612, USA;
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, MMC 609, 420 Delaware Street, Minneapolis, MN 55455, USA;
| | - Kari E. North
- Department of Epidemiology and Carolina Center for Genome Sciences, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA;
| | - Stephanie J. London
- Department of Health and Human Services, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA;
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (Y.L.); (H.C.); (E.B.)
| | - Juan C. Celedón
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15224, USA; (W.C.); (J.C.C.)
- Division of Pulmonary Medicine, Children’s Hospital of Pittsburgh of UPMC, Pittsburgh, PA 15224, USA
| | - Robert C. Kaplan
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA; (M.A.); (R.C.K.)
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (Y.L.); (H.C.); (E.B.)
- Correspondence:
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37
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Yin X, Chan LS, Bose D, Jackson AU, VandeHaar P, Locke AE, Fuchsberger C, Stringham HM, Welch R, Yu K, Fernandes Silva L, Service SK, Zhang D, Hector EC, Young E, Ganel L, Das I, Abel H, Erdos MR, Bonnycastle LL, Kuusisto J, Stitziel NO, Hall IM, Wagner GR, Kang J, Morrison J, Burant CF, Collins FS, Ripatti S, Palotie A, Freimer NB, Mohlke KL, Scott LJ, Wen X, Fauman EB, Laakso M, Boehnke M. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat Commun 2022; 13:1644. [PMID: 35347128 PMCID: PMC8960770 DOI: 10.1038/s41467-022-29143-5] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 02/23/2022] [Indexed: 01/13/2023] Open
Abstract
Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.
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Affiliation(s)
- Xianyong Yin
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Lap Sum Chan
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Debraj Bose
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Anne U. Jackson
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Peter VandeHaar
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Adam E. Locke
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA
| | - Christian Fuchsberger
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA ,grid.511439.bInstitute for Biomedicine, Eurac Research, Bolzano, 39100 Italy
| | - Heather M. Stringham
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Ryan Welch
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Ketian Yu
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Lilian Fernandes Silva
- grid.9668.10000 0001 0726 2490Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210 Finland
| | - Susan K. Service
- grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024 USA
| | - Daiwei Zhang
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA ,grid.25879.310000 0004 1936 8972Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104 USA
| | - Emily C. Hector
- grid.40803.3f0000 0001 2173 6074Department of Statistics, North Carolina State University, Raleigh, NC 27695 USA
| | - Erica Young
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA ,grid.4367.60000 0001 2355 7002Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO 63110 USA
| | - Liron Ganel
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA
| | - Indraniel Das
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA
| | - Haley Abel
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Michael R. Erdos
- grid.94365.3d0000 0001 2297 5165Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Lori L. Bonnycastle
- grid.94365.3d0000 0001 2297 5165Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Johanna Kuusisto
- grid.9668.10000 0001 0726 2490Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210 Finland ,grid.410705.70000 0004 0628 207XCenter for Medicine and Clinical Research, Kuopio University Hospital, Kuopio, 70210 Finland
| | - Nathan O. Stitziel
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA ,grid.4367.60000 0001 2355 7002Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Department of Genetics, Washington University School of Medicine, St Louis, MO 63110 USA
| | - Ira M. Hall
- grid.47100.320000000419368710Center for Genomic Health, Department of Genetics, Yale University, New Haven, CT 06510 USA
| | - Gregory R. Wagner
- grid.429438.00000 0004 0402 1933Metabolon, Inc., Morrisville, NC 27560 USA
| | | | - Jian Kang
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Jean Morrison
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Charles F. Burant
- grid.214458.e0000000086837370Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109 USA
| | - Francis S. Collins
- grid.94365.3d0000 0001 2297 5165Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Samuli Ripatti
- grid.7737.40000 0004 0410 2071Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00290 Finland ,grid.7737.40000 0004 0410 2071Department of Public Health, University of Helsinki, Helsinki, 00014 Finland ,grid.66859.340000 0004 0546 1623Broad Institute of MIT & Harvard, Cambridge, MA 02142 USA
| | - Aarno Palotie
- grid.7737.40000 0004 0410 2071Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00290 Finland ,grid.7737.40000 0004 0410 2071Department of Public Health, University of Helsinki, Helsinki, 00014 Finland ,grid.32224.350000 0004 0386 9924Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology, and Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114 USA
| | - Nelson B. Freimer
- grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024 USA
| | - Karen L. Mohlke
- grid.10698.360000000122483208Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Laura J. Scott
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Xiaoquan Wen
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Eric B. Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139 USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland.
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA.
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He S, Granot-Hershkovitz E, Zhang Y, Bressler J, Tarraf W, Yu B, Huang T, Zeng D, Wassertheil-Smoller S, Lamar M, Daviglus M, Marquine MJ, Cai J, Mosley T, Kaplan R, Boerwinkle E, Fornage M, DeCarli C, Kristal B, Gonzalez HM, Sofer T. Blood metabolites predicting mild cognitive impairment in the study of Latinos-investigation of neurocognitive aging (HCHS/SOL). ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12259. [PMID: 35229015 PMCID: PMC8865745 DOI: 10.1002/dad2.12259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 11/19/2022]
Abstract
Introduction Blood metabolomics‐based biomarkers may be useful to predict measures of neurocognitive aging. Methods We tested the association between 707 blood metabolites measured in 1451 participants from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), with mild cognitive impairment (MCI) and global cognitive change assessed 7 years later. We further used Lasso penalized regression to construct a metabolomics risk score (MRS) that predicts MCI, potentially identifying a different set of metabolites than those discovered in individual‐metabolite analysis. Results We identified 20 metabolites predicting prevalent MCI and/or global cognitive change. Six of them were novel and 14 were previously reported as associated with neurocognitive aging outcomes. The MCI MRS comprised 61 metabolites and improved prediction accuracy from 84% (minimally adjusted model) to 89% in the entire dataset and from 75% to 87% among apolipoprotein E ε4 carriers. Discussion Blood metabolites may serve as biomarkers identifying individuals at risk for MCI among US Hispanics/Latinos.
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Affiliation(s)
- Shan He
- Department of Biostatistics Harvard T.H Chan School of Public Health Boston Massachusetts USA.,Division of Sleep and Circadian Disorders Brigham and Women's Hospital Boston Massachusetts USA
| | - Einat Granot-Hershkovitz
- Division of Sleep and Circadian Disorders Brigham and Women's Hospital Boston Massachusetts USA.,Department of Medicine Harvard Medical School Boston Massachusetts USA
| | - Ying Zhang
- Division of Sleep and Circadian Disorders Brigham and Women's Hospital Boston Massachusetts USA
| | - Jan Bressler
- Human Genetics Center School of Public Health, University of Texas Health Science Center at Houston Houston Texas USA
| | - Wassim Tarraf
- Institute of Gerontology Wayne State University Detroit Michigan USA
| | - Bing Yu
- Human Genetics Center School of Public Health, University of Texas Health Science Center at Houston Houston Texas USA
| | - Tianyi Huang
- Channing Division of Network Medicine Brigham and Women's Hospital Boston Massachusetts USA
| | - Donglin Zeng
- Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill North Carolina USA
| | - Sylvia Wassertheil-Smoller
- Department of Epidemiology & Population Health Department of Pediatrics Albert Einstein College of Medicine Bronx New York USA
| | - Melissa Lamar
- Department of Medicine Institute for Minority Health Research University of Illinois at Chicago Chicago Illinois USA.,Rush Alzheimer's Disease Research Center Rush University Medical Center Chicago Illinois USA
| | - Martha Daviglus
- Department of Medicine Institute for Minority Health Research University of Illinois at Chicago Chicago Illinois USA
| | - Maria J Marquine
- Department of Psychiatry University of California, San Diego San Diego California USA
| | - Jianwen Cai
- Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill North Carolina USA
| | - Thomas Mosley
- Department of Medicine University of Mississippi Medical Center Jackson Mississippi USA
| | - Robert Kaplan
- Department of Epidemiology & Population Health Department of Pediatrics Albert Einstein College of Medicine Bronx New York USA.,Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle Washington USA
| | - Eric Boerwinkle
- Human Genetics Center School of Public Health, University of Texas Health Science Center at Houston Houston Texas USA.,Human Genome Sequencing Center Baylor College of Medicine Houston Texas USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine McGovern Medical School The University of Texas Health Science Center at Houston Houston Texas USA
| | - Charles DeCarli
- Department of Neurology, Alzheimer's Disease Center University of California, Davis Sacramento California USA
| | - Bruce Kristal
- Burke Medical Research Institute, White Plains New York USA.,Departments of Biochemistry and Neuroscience Weill Medical College of Cornell University New York New York USA
| | - Hector M Gonzalez
- Department of Neurosciences and Shiley-Marcos Alzheimer's Disease Center University of California, San Diego La Jolla California USA
| | - Tamar Sofer
- Department of Biostatistics Harvard T.H Chan School of Public Health Boston Massachusetts USA.,Division of Sleep and Circadian Disorders Brigham and Women's Hospital Boston Massachusetts USA.,Department of Medicine Harvard Medical School Boston Massachusetts USA
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Lin BM, Zhang Y, Yu B, Boerwinkle E, Thygarajan B, Yunes M, Daviglus ML, Qi Q, Kaplan R, Lash J, Cai J, Sofer T, Franceschini N. Metabolome-wide association study of estimated glomerular filtration rates in Hispanics. Kidney Int 2022; 101:144-151. [PMID: 34774559 PMCID: PMC8741745 DOI: 10.1016/j.kint.2021.09.032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 09/17/2021] [Accepted: 09/24/2021] [Indexed: 01/03/2023]
Abstract
Circulating metabolites are by-products of endogenous metabolism or exogenous sources and may inform disease states. Our study aimed to identify the source of variability in the association of metabolites with estimated glomerular filtration rate (eGFR) in Hispanics/Latinos with low chronic kidney disease prevalence by testing the association of 640 metabolites in 3,906 participants of the Hispanic Community Health Study/Study of Latinos. Metabolites were quantified in fasting serum through non-targeted mass spectrometry analysis. eGFR was regressed on inverse normally transformed metabolites in models accounting for study design and covariates. To identify the source of variation on eGFR associations, we tested the interaction of metabolites with lifestyle and clinical risk factors, and results were integrated with genotypes to identify metabolite genetic regulation. The mean age was 46 years, 43% were men, 22% were current smokers, 47% had a Caribbean Hispanic background, 19% had diabetes and the mean cohort eGFR was 96.4 ml/min/1.73 m2. We identified 404 eGFR-metabolite associations (False Discovery Rate under 0.05). Of these, 69 were previously reported, and 79 were novel associations with eGFR replicated in one or more published studies. There were significant interactions with lifestyle and clinical risk factors, with larger differences in eGFR-metabolite associations within strata of age, urine albumin to creatinine ratio, diabetes and Hispanic/Latino background. Several newly identified metabolites were genetically regulated, and variants were located at genomic regions previously associated with eGFR. Thus, our results suggest complex mechanisms contribute to the association of eGFR with metabolites and provide new insights into these associations.
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Affiliation(s)
- Bridget M. Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC
| | - Ying Zhang
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, 02115
| | - Bing Yu
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Bharat Thygarajan
- Division of Molecular Pathology and Genomics, University of Minnesota, Minneapolis, MN
| | - Milagros Yunes
- Department of Medicine, Division of Nephrology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago College of Medicine, Chicago, IL
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461.,Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle WA
| | - James Lash
- Division of Nephrology, Department of Medicine, University of Illinois, Chicago, IL
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, 02115,Departments of Medicine and Biostatistics, Harvard University, Boston, MA, 02115
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC
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40
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Fernandes Silva L, Vangipurapu J, Laakso M. The "Common Soil Hypothesis" Revisited-Risk Factors for Type 2 Diabetes and Cardiovascular Disease. Metabolites 2021; 11:metabo11100691. [PMID: 34677406 PMCID: PMC8540397 DOI: 10.3390/metabo11100691] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/21/2021] [Accepted: 10/07/2021] [Indexed: 12/12/2022] Open
Abstract
The prevalence and the incidence of type 2 diabetes (T2D), representing >90% of all cases of diabetes, are increasing rapidly worldwide. Identification of individuals at high risk of developing diabetes is of great importance, as early interventions might delay or even prevent full-blown disease. T2D is a complex disease caused by multiple genetic variants in interaction with lifestyle and environmental factors. Cardiovascular disease (CVD) is the major cause of morbidity and mortality. Detailed understanding of molecular mechanisms underlying in CVD events is still largely missing. Several risk factors are shared between T2D and CVD, including obesity, insulin resistance, dyslipidemia, and hyperglycemia. CVD can precede the development of T2D, and T2D is a major risk factor for CVD, suggesting that both conditions have common genetic and environmental antecedents and that they share “common soil”. We analyzed the relationship between the risk factors for T2D and CVD based on genetics and population-based studies with emphasis on Mendelian randomization studies.
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41
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Medwid S, Price HR, Taylor DP, Mailloux J, Schwarz UI, Kim RB, Tirona RG. Organic Anion Transporting Polypeptide 2B1 (OATP2B1) Genetic Variants: In Vitro Functional Characterization and Association With Circulating Concentrations of Endogenous Substrates. Front Pharmacol 2021; 12:713567. [PMID: 34594217 PMCID: PMC8476882 DOI: 10.3389/fphar.2021.713567] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 08/23/2021] [Indexed: 12/12/2022] Open
Abstract
Organic anion transporting polypeptide 2B1 (OATP2B1, gene SLCO2B1) is an uptake transporter that is thought to determine drug disposition and in particular, the oral absorption of medications. At present, the clinical relevance of SLCO2B1 genetic variation on pharmacokinetics is poorly understood. We sought to determine the functional activity of 5 of the most common missense OATP2B1 variants (c.76_84del, c.601G>A, c.917G>A, c.935G>A, and c.1457C>T) and a predicted dysfunctional variant (c.332G>A) in vitro. Furthermore, we measured the basal plasma concentrations of endogenous OATP2B1 substrates, namely estrone sulfate, dehydroepiandrosterone sulfate (DHEAS), pregnenolone sulfate, coproporphyrin I (CPI), and CPIII, and assessed their relationships with SLCO2B1 genotypes in 93 healthy participants. Compared to reference OATP2B1, the transport activities of the c.332G>A, c.601G>A and c.1457C>T variants were reduced among the substrates examined (estrone sulfate, DHEAS, CPI, CPIII and rosuvastatin), although there were substrate-dependent effects. Lower transport function of OATP2B1 variants could be explained by diminished cell surface expression. Other OATP2B1 variants (c.76-84del, c.917G>A and c.935G>A) had similar activity to the reference transporter. In the clinical cohort, the SLCO2B1 c.935G>A allele was associated with both higher plasma CPI (42%) and CPIII (31%) concentrations, while SLCO2B1 c.917G>A was linked to lower plasma CPIII by 28% after accounting for the effects of age, sex, and SLCO1B1 genotypes. No association was observed between SLCO2B1 variant alleles and estrone sulfate or DHEAS plasma concentrations, however 45% higher plasma pregnenolone sulfate level was associated with SLCO2B1 c.1457C>T. Taken together, we found that the impacts of OATP2B1 variants on transport activities in vitro were not fully aligned with their associations to plasma concentrations of endogenous substrates in vivo. Additional studies are required to determine whether circulating endogenous substrates reflect OATP2B1 activity.
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Affiliation(s)
- Samantha Medwid
- Department of Physiology & Pharmacology, University of Western Ontario, London, ON, Canada.,Division of Clinical Pharmacology, Department of Medicine, University of Western Ontario, London, ON, Canada
| | - Hayley R Price
- Department of Physiology & Pharmacology, University of Western Ontario, London, ON, Canada
| | - Daniel P Taylor
- Department of Physiology & Pharmacology, University of Western Ontario, London, ON, Canada
| | - Jaymie Mailloux
- Department of Physiology & Pharmacology, University of Western Ontario, London, ON, Canada.,Division of Clinical Pharmacology, Department of Medicine, University of Western Ontario, London, ON, Canada
| | - Ute I Schwarz
- Department of Physiology & Pharmacology, University of Western Ontario, London, ON, Canada.,Division of Clinical Pharmacology, Department of Medicine, University of Western Ontario, London, ON, Canada
| | - Richard B Kim
- Department of Physiology & Pharmacology, University of Western Ontario, London, ON, Canada.,Division of Clinical Pharmacology, Department of Medicine, University of Western Ontario, London, ON, Canada.,Department of Oncology, Schulich School of Medicine, University of Western Ontario, London, ON, Canada
| | - Rommel G Tirona
- Department of Physiology & Pharmacology, University of Western Ontario, London, ON, Canada.,Division of Clinical Pharmacology, Department of Medicine, University of Western Ontario, London, ON, Canada
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Lu H, Zhang J, Chen YE, Garcia-Barrio MT. Integration of Transformative Platforms for the Discovery of Causative Genes in Cardiovascular Diseases. Cardiovasc Drugs Ther 2021; 35:637-654. [PMID: 33856594 DOI: 10.1007/s10557-021-07175-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/18/2021] [Indexed: 12/11/2022]
Abstract
Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Genome-wide association studies (GWAS) are powerful epidemiological tools to find genes and variants associated with cardiovascular diseases while follow-up biological studies allow to better understand the etiology and mechanisms of disease and assign causality. Improved methodologies and reduced costs have allowed wider use of bulk and single-cell RNA sequencing, human-induced pluripotent stem cells, organoids, metabolomics, epigenomics, and novel animal models in conjunction with GWAS. In this review, we feature recent advancements relevant to cardiovascular diseases arising from the integration of genetic findings with multiple enabling technologies within multidisciplinary teams to highlight the solidifying transformative potential of this approach. Well-designed workflows integrating different platforms are greatly improving and accelerating the unraveling and understanding of complex disease processes while promoting an effective way to find better drug targets, improve drug design and repurposing, and provide insight towards a more personalized clinical practice.
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Affiliation(s)
- Haocheng Lu
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA
| | - Jifeng Zhang
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA.,Center for Advanced Models for Translational Sciences and Therapeutics, University of Michigan Medical Center, Ann Arbor, MI, 48109, USA
| | - Y Eugene Chen
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA. .,Center for Advanced Models for Translational Sciences and Therapeutics, University of Michigan Medical Center, Ann Arbor, MI, 48109, USA.
| | - Minerva T Garcia-Barrio
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA.
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