1
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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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2
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Xiao S, Li VL, Lyu X, Chen X, Wei W, Abbasi F, Knowles JW, Tung ASH, Deng S, Tiwari G, Shi X, Zheng S, Farrell L, Chen ZZ, Taylor KD, Guo X, Goodarzi MO, Wood AC, Chen YDI, Lange LA, Rich SS, Rotter JI, Clish CB, Tahir UA, Gerszten RE, Benson MD, Long JZ. Lac-Phe mediates the effects of metformin on food intake and body weight. Nat Metab 2024; 6:659-669. [PMID: 38499766 PMCID: PMC11062621 DOI: 10.1038/s42255-024-00999-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 01/30/2024] [Indexed: 03/20/2024]
Abstract
Metformin is a widely prescribed anti-diabetic medicine that also reduces body weight. There is ongoing debate about the mechanisms that mediate metformin's effects on energy balance. Here, we show that metformin is a powerful pharmacological inducer of the anorexigenic metabolite N-lactoyl-phenylalanine (Lac-Phe) in cells, in mice and two independent human cohorts. Metformin drives Lac-Phe biosynthesis through the inhibition of complex I, increased glycolytic flux and intracellular lactate mass action. Intestinal epithelial CNDP2+ cells, not macrophages, are the principal in vivo source of basal and metformin-inducible Lac-Phe. Genetic ablation of Lac-Phe biosynthesis in male mice renders animals resistant to the effects of metformin on food intake and body weight. Lastly, mediation analyses support a role for Lac-Phe as a downstream effector of metformin's effects on body mass index in participants of a large population-based observational cohort, the Multi-Ethnic Study of Atherosclerosis. Together, these data establish Lac-Phe as a critical mediator of the body weight-lowering effects of metformin.
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Affiliation(s)
- Shuke Xiao
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Veronica L Li
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Department of Chemistry, Stanford University, Stanford, CA, USA
- Wu Tsai Human Performance Alliance, Stanford University, Stanford, CA, USA
| | - Xuchao Lyu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Wu Tsai Human Performance Alliance, Stanford University, Stanford, CA, USA
| | - Xudong Chen
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Wei Wei
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Fahim Abbasi
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joshua W Knowles
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Alan Sheng-Hwa Tung
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Gaurav Tiwari
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shuning Zheng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Laurie Farrell
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Kent D Taylor
- 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
| | - Xiuqing Guo
- 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
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alexis C Wood
- USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Yii-Der Ida Chen
- 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
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, 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
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Mark D Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jonathan Z Long
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA.
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA.
- Wu Tsai Human Performance Alliance, Stanford University, Stanford, CA, USA.
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.
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3
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Peng Q, Gilder DA, Bernert RA, Karriker-Jaffe KJ, Ehlers CL. Genetic factors associated with suicidal behaviors and alcohol use disorders in an American Indian population. Mol Psychiatry 2024; 29:902-913. [PMID: 38177348 PMCID: PMC11176067 DOI: 10.1038/s41380-023-02379-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 01/06/2024]
Abstract
American Indians (AI) demonstrate the highest rates of both suicidal behaviors (SB) and alcohol use disorders (AUD) among all ethnic groups in the US. Rates of suicide and AUD vary substantially between tribal groups and across different geographical regions, underscoring a need to delineate more specific risk and resilience factors. Using data from over 740 AI living within eight contiguous reservations, we assessed genetic risk factors for SB by investigating: (1) possible genetic overlap with AUD, and (2) impacts of rare and low-frequency genomic variants. Suicidal behaviors included lifetime history of suicidal thoughts and acts, including verified suicide deaths, scored using a ranking variable for the SB phenotype (range 0-4). We identified five loci significantly associated with SB and AUD, two of which are intergenic and three intronic on genes AACSP1, ANK1, and FBXO11. Nonsynonymous rare and low-frequency mutations in four genes including SERPINF1 (PEDF), ZNF30, CD34, and SLC5A9, and non-intronic rare and low-frequency mutations in genes OPRD1, HSD17B3 and one lincRNA were significantly associated with SB. One identified pathway related to hypoxia-inducible factor (HIF) regulation, whose 83 nonsynonymous rare and low-frequency variants on 10 genes were significantly linked to SB as well. Four additional genes, and two pathways related to vasopressin-regulated water metabolism and cellular hexose transport, also were strongly associated with SB. This study represents the first investigation of genetic factors for SB in an American Indian population that has high risk for suicide. Our study suggests that bivariate association analysis between comorbid disorders can increase statistical power; and rare and low-frequency variant analysis in a high-risk population enabled by whole-genome sequencing has the potential to identify novel genetic factors. Although such findings may be population specific, rare functional mutations relating to PEDF and HIF regulation align with past reports and suggest a biological mechanism for suicide risk and a potential therapeutic target for intervention.
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Affiliation(s)
- Qian Peng
- Department of Neuroscience, The Scripps Research Institute, La Jolla, CA, USA.
| | - David A Gilder
- Department of Neuroscience, The Scripps Research Institute, La Jolla, CA, USA
| | - Rebecca A Bernert
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | | | - Cindy L Ehlers
- Department of Neuroscience, The Scripps Research Institute, La Jolla, CA, USA
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4
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Louck LE, Cara KC, Klatt K, Wallace TC, Chung M. The Relationship of Circulating Choline and Choline-Related Metabolite Levels with Health Outcomes: A Scoping Review of Genome-Wide Association Studies and Mendelian Randomization Studies. Adv Nutr 2024; 15:100164. [PMID: 38128611 PMCID: PMC10819410 DOI: 10.1016/j.advnut.2023.100164] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/11/2023] [Accepted: 12/18/2023] [Indexed: 12/23/2023] Open
Abstract
Choline is essential for proper liver, muscle, brain, lipid metabolism, cellular membrane composition, and repair. Understanding genetic determinants of circulating choline metabolites can help identify new determinants of choline metabolism, requirements, and their link to disease endpoints. We conducted a scoping review to identify studies assessing the association of genetic polymorphisms on circulating choline and choline-related metabolite concentrations and subsequent associations with health outcomes. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement scoping review extension. Literature was searched to September 28, 2022, in 4 databases: Embase, MEDLINE, Web of Science, and the Biological Science Index. Studies of any duration in humans were considered. Any genome-wide association study (GWAS) investigating genetic variant associations with circulating choline and/or choline-related metabolites and any Mendelian randomization (MR) study investigating the association of genetically predicted circulating choline and/or choline-related metabolites with any health outcome were considered. Qualitative evidence is presented in summary tables. From 1248 total reviewed articles, 53 were included (GWAS = 27; MR = 26). Forty-two circulating choline-related metabolites were tested in association with genetic variants in GWAS studies, primarily trimethylamine N-oxide, betaine, sphingomyelins, lysophosphatidylcholines, and phosphatidylcholines. MR studies investigated associations between 52 total unique choline metabolites and 66 unique health outcomes. Of these, 47 significant associations were reported between 16 metabolites (primarily choline, lysophosphatidylcholines, phosphatidylcholines, betaine, and sphingomyelins) and 27 health outcomes including cancer, cardiovascular, metabolic, bone, and brain-related outcomes. Some articles reported significant associations between multiple choline types and the same health outcome. Genetically predicted circulating choline and choline-related metabolite concentrations are associated with a wide variety of health outcomes. Further research is needed to assess how genetic variability influences choline metabolism and whether individuals with lower genetically predicted circulating choline and choline-related metabolite concentrations would benefit from a dietary intervention or supplementation.
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Affiliation(s)
- Lauren E Louck
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States
| | - Kelly C Cara
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States
| | - Kevin Klatt
- Nutritional Sciences and Toxicology, University of California, Berkeley, CA, United States
| | - Taylor C Wallace
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States; Think Health Group, Inc, Washington, DC, United States
| | - Mei Chung
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States.
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5
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Baron C, Cherkaoui S, Therrien-Laperriere S, Ilboudo Y, Poujol R, Mehanna P, Garrett ME, Telen MJ, Ashley-Koch AE, Bartolucci P, Rioux JD, Lettre G, Rosiers CD, Ruiz M, Hussin JG. Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results. iScience 2023; 26:108473. [PMID: 38077122 PMCID: PMC10709128 DOI: 10.1016/j.isci.2023.108473] [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: 03/30/2023] [Revised: 08/24/2023] [Accepted: 11/13/2023] [Indexed: 12/20/2023] Open
Abstract
Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehensive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks.
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Affiliation(s)
- Cantin Baron
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
| | - Sarah Cherkaoui
- Montreal Heart Institute, Montréal, QC, Canada
- Division of Oncology and Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Center, Université Paris-Saclay, Villejuif, France
| | | | - Yann Ilboudo
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
| | | | | | - Melanie E. Garrett
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Marilyn J. Telen
- Division of Hematology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | | | - Pablo Bartolucci
- Université Paris Est Créteil, Hôpitaux Universitaires Henri Mondor, APHP, Sickle cell referral center – UMGGR, Créteil, France
- Université Paris Est Créteil, IMRB, Laboratory of excellence LABEX, Créteil, France
| | - John D. Rioux
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Médecine, Université de Montréal, Montréal, QC, Canada
| | - Guillaume Lettre
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Médecine, Université de Montréal, Montréal, QC, Canada
| | - Christine Des Rosiers
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Nutrition, Université de Montréal, Montréal, QC, Canada
| | - Matthieu Ruiz
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Nutrition, Université de Montréal, Montréal, QC, Canada
| | - Julie G. Hussin
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Médecine, Université de Montréal, Montréal, QC, Canada
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6
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Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, Laird JR, Paraskevas KI, Ruzsa Z, Singh N, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh I, Teji JS, Al-Maini M, Isenovic ER, Viswanathan V, Khanna P, Fouda MM, Saba L, Suri JS. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. J Korean Med Sci 2023; 38:e395. [PMID: 38013648 PMCID: PMC10681845 DOI: 10.3346/jkms.2023.38.e395] [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: 07/31/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
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Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
- Asia Pacific Vascular Society, New Delhi, India
| | - Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Bennett University, Greater Noida, India
| | - Mahesh Maindarkar
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- School of Bioengineering Sciences and Research, Maharashtra Institute of Technology's Art, Design and Technology University, Pune, India
| | | | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura Mentella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | | | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Inder Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Mostafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, Beograd, Serbia
| | | | - Puneet Khanna
- Department of Anaesthesiology, AIIMS, New Delhi, India
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Jasjit S Suri
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, India.
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7
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Xiao S, Li VL, Lyu X, Chen X, Wei W, Abbasi F, Knowles JW, Deng S, Tiwari G, Shi X, Zheng S, Farrell L, Chen ZZ, Taylor KD, Guo X, Goodarzi MO, Wood AC, Ida Chen YD, Lange LA, Rich SS, Rotter JI, Clish CB, Tahir UA, Gerszten RE, Benson MD, Long JZ. Lac-Phe mediates the anti-obesity effect of metformin. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.02.565321. [PMID: 37961394 PMCID: PMC10635077 DOI: 10.1101/2023.11.02.565321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Metformin is a widely prescribed anti-diabetic medicine that also reduces body weight. The mechanisms that mediate metformin's effects on energy balance remain incompletely defined. Here we show that metformin is a powerful pharmacological inducer of the anorexigenic metabolite Lac-Phe in mice as well as in two independent human cohorts. In cell culture, metformin drives Lac-Phe biosynthesis via inhibition of complex I, increased glycolytic flux, and intracellular lactate mass action. Other biguanides and structurally distinct inhibitors of oxidative phosphorylation also increase Lac-Phe levels in vitro. Genetic ablation of CNDP2, the principal biosynthetic enzyme for Lac-Phe, in mice renders animals resistant to metformin's anorexigenic and anti-obesity effects. Mediation analyses also support a role for Lac-Phe in metformin's effect on body mass index in humans. These data establish the CNDP2/Lac-Phe pathway as a critical mediator of the effects of metformin on energy balance.
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Affiliation(s)
- Shuke Xiao
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Veronica L. Li
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Chemistry, Stanford University, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Wu Tsai Human Performance Alliance, Stanford University, Stanford, CA, USA
| | - Xuchao Lyu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Wu Tsai Human Performance Alliance, Stanford University, Stanford, CA, USA
| | - Xudong Chen
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Wei Wei
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
| | - Fahim Abbasi
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joshua W. Knowles
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Gaurav Tiwari
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Shuning Zheng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Laurie Farrell
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Mark O. Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Alexis C. Wood
- USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA
| | - 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
| | | | - Usman A. Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Broad Institute of Harvard and MIT, Cambridge, MA
| | - Mark D. Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Jonathan Z. Long
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Human Performance Alliance, Stanford University, Stanford, CA, USA
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8
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Vera CD, López AR, Ewaneewane AS, Lewis K, Parmisano S, Mondejar-Parreño G, Upadhyaya C, Mullen M. Disparities in cardio-oncology: Implication of angiogenesis, inflammation, and chemotherapy. Life Sci 2023; 332:122106. [PMID: 37730108 DOI: 10.1016/j.lfs.2023.122106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 08/31/2023] [Accepted: 09/17/2023] [Indexed: 09/22/2023]
Abstract
Cancers and cardiovascular diseases are the top two causes of death in the United States. Over the past decades, novel therapies have slowed the cancer mortality rate, yet cardiac failures have risen due to the toxicity of cancer treatments. The mechanisms behind this relationship are poorly understood and it is crucial that we properly treat patients at risk of developing cardiac failure in response to cancer treatments. Currently, we rely on early-stage biomarkers of inflammation and angiogenesis to detect cardiotoxicity before it becomes irreversible. Identification of such biomarkers allows healthcare professionals to decrease the adverse effects of cancer therapies. Angiogenesis and inflammation have a systemic influence on the heart and vasculature following cancer therapy. In the field of cardio-oncology, there has been a recent emphasis on gender and racial disparities in cardiotoxicity and the impact of these disparities on disease outcomes, but there is a scarcity of data on how cardiotoxicity varies across diverse populations. Here, we will discuss how current markers of angiogenesis and inflammation induced by cancer therapy are related to disparities in cardiovascular health.
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Affiliation(s)
- Carlos D Vera
- Stanford Cardiovascular Institute, Stanford School of Medicine, Stanford, CA, USA
| | - Agustín Rodríguez López
- Stanford Cardiovascular Institute, Stanford School of Medicine, Stanford, CA, USA; University of Puerto Rico Medical Science Campus, Rio Piedras, PR, USA
| | - Alex S Ewaneewane
- Stanford Cardiovascular Institute, Stanford School of Medicine, Stanford, CA, USA; Meharry Medical College, Nashville, TN, USA
| | - Kasey Lewis
- Stanford Cardiovascular Institute, Stanford School of Medicine, Stanford, CA, USA; Lehigh University, Bethlehem, PA, USA
| | - Sophia Parmisano
- Stanford Cardiovascular Institute, Stanford School of Medicine, Stanford, CA, USA; University of California San Diego, San Diego, CA, USA
| | | | | | - McKay Mullen
- Stanford Cardiovascular Institute, Stanford School of Medicine, Stanford, CA, USA.
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9
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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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10
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Reus LM, Boltz T, Francia M, Bot M, Ramesh N, Koromina M, Pijnenburg YAL, den Braber A, van der Flier WM, Visser PJ, van der Lee SJ, Tijms BM, Teunissen CE, Loohuis LO, Ophoff RA. Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.26.559021. [PMID: 37808647 PMCID: PMC10557608 DOI: 10.1101/2023.09.26.559021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Genomic studies of molecular traits have provided mechanistic insights into complex disease, though these lag behind for brain-related traits due to the inaccessibility of brain tissue. We leveraged cerebrospinal fluid (CSF) to study neurobiological mechanisms in vivo , measuring 5,543 CSF metabolites, the largest panel in CSF to date, in 977 individuals of European ancestry. Individuals originated from two separate cohorts including cognitively healthy subjects (n=490) and a well-characterized memory clinic sample, the Amsterdam Dementia Cohort (ADC, n=487). We performed metabolite quantitative trait loci (mQTL) mapping on CSF metabolomics and found 126 significant mQTLs, representing 65 unique CSF metabolites across 51 independent loci. To better understand the role of CSF mQTLs in brain-related disorders, we performed a metabolome-wide association study (MWAS), identifying 40 associations between CSF metabolites and brain traits. Similarly, over 90% of significant mQTLs demonstrated colocalized associations with brain-specific gene expression, unveiling potential neurobiological pathways.
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11
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Benson MD, Eisman AS, Tahir UA, Katz DH, Deng S, Ngo D, Robbins JM, Hofmann A, Shi X, Zheng S, Keyes M, Yu Z, Gao Y, Farrell L, Shen D, Chen ZZ, Cruz DE, Sims M, Correa A, Tracy RP, Durda P, Taylor KD, Liu Y, Johnson WC, Guo X, Yao J, Chen YDI, Manichaikul AW, Jain D, Yang Q, Bouchard C, Sarzynski MA, Rich SS, Rotter JI, Wang TJ, Wilson JG, Clish CB, Sarkar IN, Natarajan P, Gerszten RE. Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma. Cell Metab 2023; 35:1646-1660.e3. [PMID: 37582364 PMCID: PMC11118091 DOI: 10.1016/j.cmet.2023.07.012] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 04/12/2023] [Accepted: 07/24/2023] [Indexed: 08/17/2023]
Abstract
Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.
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Affiliation(s)
- Mark D Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Aaron S Eisman
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Center for Biomedical Informatics, Brown University, Providence, RI, USA
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daniel H Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Debby Ngo
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jeremy M Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Alissa Hofmann
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shuning Zheng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michelle Keyes
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zhi Yu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yan Gao
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Laurie Farrell
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Dongxiao Shen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daniel E Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mario Sims
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Adolfo Correa
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Russell P Tracy
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Peter Durda
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Kent D Taylor
- 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
| | - Yongmei Liu
- Department of Medicine, Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Xiuqing Guo
- 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
| | - Jie Yao
- 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
| | - Yii-Der Ida Chen
- 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
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA; Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | | | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Claude Bouchard
- Human Genomic Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Mark A Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, Columbia, SC, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, 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
| | - Thomas J Wang
- Department of Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Indra Neil Sarkar
- Center for Biomedical Informatics, Brown University, Providence, RI, USA
| | - Pradeep Natarajan
- Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine Harvard Medical School, Boston, MA, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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12
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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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13
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Gilder D, Bernert R, Karriker-Jaffe K, Ehlers C, Peng Q. Genetic Factors Associated with Suicidal Behaviors and Alcohol Use Disorders in an American Indian Population. RESEARCH SQUARE 2023:rs.3.rs-2950284. [PMID: 37398076 PMCID: PMC10312956 DOI: 10.21203/rs.3.rs-2950284/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
American Indians (AI) demonstrate the highest rates of both suicidal behaviors (SB) and alcohol use disorders (AUD) among all ethnic groups in the US. Rates of suicide and AUD vary substantially between tribal groups and across different geographical regions, underscoring a need to delineate more specific risk and resilience factors. Using data from over 740 AI living within eight contiguous reservations, we assessed genetic risk factors for SB by investigating: (1) possible genetic overlap with AUD, and (2) impacts of rare and low frequency genomic variants. Suicidal behaviors included lifetime history of suicidal thoughts and acts, including verified suicide deaths, scored using a ranking variable for the SB phenotype (range 0-4). We identified five loci significantly associated with SB and AUD, two of which are intergenic and three intronic on genes AACSP1, ANK1, and FBXO11. Nonsynonymous rare mutations in four genes including SERPINF1 (PEDF), ZNF30, CD34, and SLC5A9, and non-intronic rare mutations in genes OPRD1, HSD17B3 and one lincRNA were significantly associated with SB. One identified pathway related to hypoxia-inducible factor (HIF) regulation, whose 83 nonsynonymous rare variants on 10 genes were significantly linked to SB as well. Four additional genes, and two pathways related to vasopressin-regulated water metabolism and cellular hexose transport, also were strongly associated with SB. This study represents the first investigation of genetic factors for SB in an American Indian population that has high risk for suicide. Our study suggests that bivariate association analysis between comorbid disorders can increase statistical power; and rare variant analysis in a high-risk population enabled by whole-genome sequencing has the potential to identify novel genetic factors. Although such findings may be population specific, rare functional mutations relating to PEDF and HIF regulation align with past reports and suggest a biological mechanism for suicide risk and a potential therapeutic target for intervention.
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14
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Tanzo JT, Li VL, Wiggenhorn AL, Moya-Garzon MD, Wei W, Lyu X, Dong W, Tahir UA, Chen ZZ, Cruz DE, Deng S, Shi X, Zheng S, Guo Y, Sims M, Abu-Remaileh M, Wilson JG, Gerszten RE, Long JZ, Benson MD. CYP4F2 is a human-specific determinant of circulating N-acyl amino acid levels. J Biol Chem 2023:104764. [PMID: 37121548 DOI: 10.1016/j.jbc.2023.104764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/21/2023] [Accepted: 04/23/2023] [Indexed: 05/02/2023] Open
Abstract
N-acyl amino acids are a large family of circulating lipid metabolites that modulate energy expenditure and fat mass in rodents. However, little is known about the regulation and potential cardiometabolic functions of N-acyl amino acids in humans. Here, we analyze the cardiometabolic phenotype associations and genomic associations of four plasma N-acyl amino acids (N-oleoyl-leucine, N-oleoyl-phenylalanine, N-oleoyl-serine, and N-oleoyl-glycine) in 2,351 individuals from the Jackson Heart Study. We find that plasma levels of specific N-acyl amino acids are associated with cardiometabolic disease endpoints independent of free amino acid plasma levels and in patterns according to the amino acid head group. By integrating whole genome sequencing data with N-acyl amino acid levels, we identify that the genetic determinants of N-acyl amino acid levels also cluster according to amino acid head group. Furthermore, we identify the CYP4F2 locus as a genetic determinant of plasma N-oleoyl-leucine and N-oleoyl-phenylalanine levels in human plasma. In experimental studies, we demonstrate that CYP4F2-mediated hydroxylation of N-oleoyl-leucine and N-oleoyl-phenylalanine results in metabolic diversification and production of many previously unknown lipid metabolites with varying characteristics of the fatty acid tail group, including several that structurally resemble fatty acid hydroxy fatty acids (FAHFAs). These studies provide a structural framework for understanding the regulation and disease-associations of N-acyl amino acids in humans and identify that the diversity of this lipid signaling family can be significantly expanded through CYP4F-mediated ω-hydroxylation.
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Affiliation(s)
- Julia T Tanzo
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Veronica L Li
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Department of Chemistry, Stanford University, Stanford, CA, USA; Wu Tsai Human Performance Alliance, Stanford University, CA, USA
| | - Amanda L Wiggenhorn
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Maria Dolores Moya-Garzon
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA
| | - Wei Wei
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Department of Biology, Stanford University, Stanford, CA, USA
| | - Xuchao Lyu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Wu Tsai Human Performance Alliance, Stanford University, CA, USA
| | - Wentao Dong
- Stanford ChEM-H, Stanford University, Stanford, CA, USA; Department of Chemical Engineering, Stanford University, Stanford, CA, USA
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Daniel E Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Shuning Zheng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Yan Guo
- Univ of Mississippi Medical Center, Jackson, MS
| | - Mario Sims
- Univ of Mississippi Medical Center, Jackson, MS
| | - Monther Abu-Remaileh
- Stanford ChEM-H, Stanford University, Stanford, CA, USA; Department of Chemical Engineering, Stanford University, Stanford, CA, USA
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; Broad Institute of Harvard and MIT, Cambridge, MA
| | - Jonathan Z Long
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA; Wu Tsai Human Performance Alliance, Stanford University, CA, USA.
| | - Mark D Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
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15
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Fine KS, Wilkins JT, Sawicki KT. Clinical Metabolomic Landscape of Cardiovascular Physiology and Disease. J Am Heart Assoc 2023; 12:e027725. [PMID: 36892040 PMCID: PMC10111533 DOI: 10.1161/jaha.122.027725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Affiliation(s)
- Keenan S Fine
- Feinberg School of Medicine Northwestern University Chicago IL USA
| | - John T Wilkins
- Department of Preventive Medicine, Feinberg School of Medicine Northwestern University Chicago IL USA
- Division of Cardiology, Department of Medicine, Feinberg School of Medicine Northwestern University Chicago IL USA
| | - Konrad Teodor Sawicki
- Department of Preventive Medicine, Feinberg School of Medicine Northwestern University Chicago IL USA
- Division of Cardiology, Department of Medicine, Feinberg School of Medicine Northwestern University Chicago IL USA
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Tanzo JT, Li VL, Wiggenhorn AL, Moya-Garzon MD, Wei W, Lyu X, Dong W, Tahir UA, Chen ZZ, Cruz DE, Deng S, Shi X, Zheng S, Guo Y, Sims M, Abu-Remaileh M, Wilson JG, Gerszten RE, Long JZ, Benson MD. CYP4F2 is a human-specific determinant of circulating N-acyl amino acid levels. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.09.531581. [PMID: 36945562 PMCID: PMC10028954 DOI: 10.1101/2023.03.09.531581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
N-acyl amino acids are a large family of circulating lipid metabolites that modulate energy expenditure and fat mass in rodents. However, little is known about the regulation and potential cardiometabolic functions of N-acyl amino acids in humans. Here, we analyze the cardiometabolic phenotype associations and genetic regulation of four plasma N-fatty acyl amino acids (N-oleoyl-leucine, N-oleoyl-phenylalanine, N-oleoyl-serine, and N-oleoyl-glycine) in 2,351 individuals from the Jackson Heart Study. N-oleoyl-leucine and N-oleoyl-phenylalanine were positively associated with traits related to energy balance, including body mass index, waist circumference, and subcutaneous adipose tissue. In addition, we identify the CYP4F2 locus as a human-specific genetic determinant of plasma N-oleoyl-leucine and N-oleoyl-phenylalanine levels. In vitro, CYP4F2-mediated hydroxylation of N-oleoyl-leucine and N-oleoyl-phenylalanine results in metabolic diversification and production of many previously unknown lipid metabolites with varying characteristics of the fatty acid tail group, including several that structurally resemble fatty acid hydroxy fatty acids (FAHFAs). By contrast, FAAH-regulated N-oleoyl-glycine and N-oleoyl-serine were inversely associated with traits related to glucose and lipid homeostasis. These data uncover a human-specific enzymatic node for the metabolism of a subset of N-fatty acyl amino acids and establish a framework for understanding the cardiometabolic roles of individual N-fatty acyl amino acids in humans.
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Soremekun C, Machipisa T, Soremekun O, Pirie F, Oyekanmi N, Motala AA, Chikowore T, Fatumo S. Multivariate GWAS analysis reveals loci associated with liver functions in continental African populations. PLoS One 2023; 18:e0280344. [PMID: 36809439 PMCID: PMC9942994 DOI: 10.1371/journal.pone.0280344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/27/2022] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND Liver disease is any condition that causes liver damage and inflammation and may likely affect the function of the liver. Vital biochemical screening tools that can be used to evaluate the health of the liver and help diagnose, prevent, monitor, and control the development of liver disease are known as liver function tests (LFT). LFTs are performed to estimate the level of liver biomarkers in the blood. Several factors are associated with differences in concentration levels of LFTs in individuals, such as genetic and environmental factors. The aim of our study was to identify genetic loci associated with liver biomarker levels with a shared genetic basis in continental Africans, using a multivariate genome-wide association study (GWAS) approach. METHODS We used two distinct African populations, the Ugandan Genome Resource (UGR = 6,407) and South African Zulu cohort (SZC = 2,598). The six LFTs used in our analysis were: aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), total bilirubin, and albumin. A multivariate GWAS of LFTs was conducted using the exact linear mixed model (mvLMM) approach implemented in GEMMA and the resulting P-values were presented in Manhattan and quantile-quantile (QQ) plots. First, we attempted to replicate the findings of the UGR cohort in SZC. Secondly, given that the genetic architecture of UGR is different from that of SZC, we further undertook similar analysis in the SZC and discussed the results separately. RESULTS A total of 59 SNPs reached genome-wide significance (P = 5x10-8) in the UGR cohort and with 13 SNPs successfully replicated in SZC. These included a novel lead SNP near the RHPN1 locus (lead SNP rs374279268, P-value = 4.79x10-9, Effect Allele Frequency (EAF) = 0.989) and a lead SNP at the RGS11 locus (lead SNP rs148110594, P-value = 2.34x10-8, EAF = 0.928). 17 SNPs were significant in the SZC, while all the SNPs fall within a signal on chromosome 2, rs1976391 mapped to UGT1A was identified as the lead SNP within this region. CONCLUSIONS Using multivariate GWAS method improves the power to detect novel genotype-phenotype associations for liver functions not found with the standard univariate GWAS in the same dataset.
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Affiliation(s)
- Chisom Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
- Department of Immunology and Molecular Biology, College of Health Science, Makerere University, Kampala, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Tafadzwa Machipisa
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Department of Medicine, Hatter Institute for Cardiovascular Diseases Research in Africa and Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada
| | - Opeyemi Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
- Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa
| | - Fraser Pirie
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa
| | - Nashiru Oyekanmi
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Ayesha A. Motala
- Department of Diabetes and Endocrinology, University of KwaZulu-Natal, Durban, South Africa
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Department of Pediatrics, MRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI, and LSHTM, Entebbe, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- * E-mail:
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