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Beyene HB, Huynh K, Wang T, Paul S, Cinel M, Mellett NA, Olshansky G, Meikle TG, Watts GF, Hung J, Hui J, Beilby J, Blangero J, Moses EK, Shaw JE, Magliano DJ, Giles C, Meikle PJ. Development and validation of a plasmalogen score as an independent modifiable marker of metabolic health: population based observational studies and a placebo-controlled cross-over study. EBioMedicine 2024; 105:105187. [PMID: 38861870 PMCID: PMC11215217 DOI: 10.1016/j.ebiom.2024.105187] [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: 02/12/2024] [Revised: 05/21/2024] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Decreased levels of circulating ethanolamine plasmalogens [PE(P)], and a concurrent increase in phosphatidylethanolamine (PE) are consistently reported in various cardiometabolic conditions. Here we devised, a plasmalogen score (Pls Score) that mirrors a metabolic signal that encompasses the levels of PE(P) and PE and captures the natural variation in circulating plasmalogens and perturbations in their metabolism associated with disease, diet, and lifestyle. METHODS We utilised, plasma lipidomes from the Australian Obesity, Diabetes and Lifestyle study (AusDiab; n = 10,339, 55% women) a nationwide cohort, to devise the Pls Score and validated this in the Busselton Health Study (BHS; n = 4,492, 56% women, serum lipidome) and in a placebo-controlled crossover trial involving Shark Liver Oil (SLO) supplementation (n = 10, 100% men). We examined the association of the Pls Score with cardiometabolic risk factors, type 2 diabetes mellitus (T2DM), cardiovascular disease and all-cause mortality (over 17 years). FINDINGS In a model, adjusted for age, sex and BMI, individuals in the top quintile of the Pls Score (Q5) relative to Q1 had an OR of 0.31 (95% CI 0.21-0.43), 0.39 (95% CI 0.25-0.61) and 0.42 (95% CI 0.30-0.57) for prevalent T2DM, incident T2DM and prevalent cardiovascular disease respectively, and a 34% lower mortality risk (HR = 0.66; 95% CI 0.56-0.78). Significant associations between diet and lifestyle habits and Pls Score exist and these were validated through dietary supplementation of SLO that resulted in a marked change in the Pls Score. INTERPRETATION The Pls Score as a measure that captures the natural variation in circulating plasmalogens, was not only inversely related to cardiometabolic risk and all-cause mortality but also associate with diet and lifestyle. Our results support the potential utility of the Pls Score as a biomarker for metabolic health and its responsiveness to dietary interventions. Further research is warranted to explore the underlying mechanisms and optimise the practical implementation of the Pls Score in clinical and population settings. FUNDING National Health and Medical Research Council (NHMRC grant 233200), National Health and Medical Research Council of Australia (Project grant APP1101320), Health Promotion Foundation of Western Australia, and National Health and Medical Research Council of Australia Senior Research Fellowship (#1042095).
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Affiliation(s)
- Habtamu B Beyene
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Sudip Paul
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Michelle Cinel
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | | | | | - Thomas G Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
| | - Gerald F Watts
- Medical School, University of Western Australia, Perth, WA, Australia; Cardiometabolic Service, Department of Cardiology and Internal Medicine, Royal Perth Hospital, Perth, WA, Australia
| | - Joseph Hung
- Medical School, University of Western Australia, Perth, WA, Australia
| | - Jennie Hui
- PathWest Laboratory Medicine of Western Australia, Queen Elizabeth II Medical Centre, Nedlands, WA, Australia; School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
| | - John Beilby
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric K Moses
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia; Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Dianna J Magliano
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia.
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia.
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Wang T, Beyene HB, Yi C, Cinel M, Mellett NA, Olshansky G, Meikle TG, Wu J, Dakic A, Watts GF, Hung J, Hui J, Beilby J, Blangero J, Kaddurah-Daouk R, Salim A, Moses EK, Shaw JE, Magliano DJ, Huynh K, Giles C, Meikle PJ. A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age. EBioMedicine 2024; 105:105199. [PMID: 38905750 DOI: 10.1016/j.ebiom.2024.105199] [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: 01/02/2024] [Revised: 05/30/2024] [Accepted: 05/30/2024] [Indexed: 06/23/2024] Open
Abstract
BACKGROUND Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health. METHODS Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status. FINDINGS Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have cardiovascular disease (OR = 2.13, 95% CI = 1.62-2.83), had a 2.01-fold increased risk of 12-year incident cardiovascular events (HR = 2.01, 95% CI = 1.45-2.57), and a 1.56-fold increased risk of 17-year all-cause mortality (HR = 1.56, 95% CI = 1.34-1.79), relative to the individuals in the bottom quintile group (Q1). Survival analysis further revealed that men in the Q5 group faced the challenge of reaching a median survival rate due to cardiovascular events more than six years earlier and reaching a median survival rate due to all-cause mortality more than four years earlier than men in the Q1 group. INTERPRETATION Our findings demonstrate that the mAge score captures age-related metabolic changes, predicts health outcomes, and has the potential to identify individuals at increased risk of metabolic diseases. FUNDING The specific funding of this article is provided in the acknowledgements section.
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Affiliation(s)
- Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia
| | - Habtamu B Beyene
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Changyu Yi
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Michelle Cinel
- Baker Heart and Diabetes Institute, Melbourne, Australia
| | | | | | - Thomas G Meikle
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Jingqin Wu
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | | | - Gerald F Watts
- School of Medicine, University of Western Australia, Perth, Australia; Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, Australia
| | - Joseph Hung
- School of Medicine, University of Western Australia, Perth, Australia
| | - Jennie Hui
- PathWest Laboratory Medicine of Western Australia, Nedlands, Western Australia, Australia; School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia; School of Biomedical Sciences, University of Western Australia, Australia
| | - John Beilby
- PathWest Laboratory Medicine of Western Australia, Nedlands, Western Australia, Australia; School of Biomedical Sciences, University of Western Australia, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioural Sciences, Duke University, Durham, NC, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, USA; Department of Medicine, Duke University, Durham, NC, USA
| | - Agus Salim
- Baker Heart and Diabetes Institute, Melbourne, Australia; Melbourne School of Population and Global Health School of Mathematics and Statistics, The University of Melbourne, Australia
| | - Eric K Moses
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | | | | | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia.
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MacCarthy G, Pazoki R. Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank. J Clin Med 2024; 13:2955. [PMID: 38792496 PMCID: PMC11122671 DOI: 10.3390/jcm13102955] [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: 03/18/2024] [Revised: 05/01/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.
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Affiliation(s)
- Gideon MacCarthy
- Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London UB8 3PH, UK
| | - Raha Pazoki
- Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London UB8 3PH, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, St Mary’s Campus, Norfolk Place, Imperial College London, London W2 1PG, UK
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Akbarzadeh M, Riahi P, Saeidian AH, Zarkesh M, Masjoudi S, Asgarian S, Guity K, Moheimani H, Masoudi H, Roudbar MA, Khalili D, Hosseinpanah F, Barzin M, Hogan CT, Hakonarson H, Hedayati M, Daneshpour MS, Azizi F. The Tehran longitudinal family-based cardiometabolic cohort study sheds new light on dyslipidemia transmission patterns. Sci Rep 2024; 14:4739. [PMID: 38413617 PMCID: PMC10899171 DOI: 10.1038/s41598-024-53504-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 02/01/2024] [Indexed: 02/29/2024] Open
Abstract
Dyslipidemia, as a metabolic risk factor, with the strongest and most heritable independent cause of cardiovascular diseases worldwide. We investigated the familial transmission patterns of dyslipidemia through a longitudinal family-based cohort, the Tehran Cardiometabolic Genetic Study (TCGS) in Iran. We enrolled 18,729 individuals (45% were males) aged > 18 years (mean: 38.15 (15.82)) and observed them over five 3-year follow-up periods. We evaluated the serum concentrations of total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol with the first measurement among longitudinal measures and the average measurements (AM) of the five periods. Heritability analysis was conducted using a mixed-effect framework with likelihood-based and Bayesian approaches. The periodic prevalence and heritability of dyslipidemia were estimated to be 65.7 and 42%, respectively. The likelihood of an individual having at least one dyslipidemic parent reveals an OR = 6.94 (CI 5.28-9.30) compared to those who do not have dyslipidemic parents. The most considerable intraclass correlation of family members was for the same-sex siblings, with ICC ~ 25.5%. For serum concentrations, heritability ranged from 33.64 to 60.95%. Taken together, these findings demonstrate that familial transmission of dyslipidemia in the Tehran population is strong, especially within the same-gender siblings. According to previous reports, the heritability of dyslipidemia in this population is considerably higher than the global average.
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Affiliation(s)
- Mahdi Akbarzadeh
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Parisa Riahi
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amir Hossein Saeidian
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd, Abramson Building, Philadelphia, PA, 19104, USA
| | - Maryam Zarkesh
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sajedeh Masjoudi
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sara Asgarian
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Kamran Guity
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamed Moheimani
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Homayoon Masoudi
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahmoud Amiri Roudbar
- Department of Animal Science, Safiabad-Dezful Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education & Extension Organization (AREEO), Dezful, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farhad Hosseinpanah
- Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Barzin
- Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Carolyn T Hogan
- Division of Hepatology, Temple University Hospital, Philadelphia, PA, USA
| | - Hakon Hakonarson
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd, Abramson Building, Philadelphia, PA, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Mehdi Hedayati
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam S Daneshpour
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Leite JMRS, Pereira JL, Damasceno NRT, Soler JMP, Fisberg RM, Rogero MM, Sarti FM. Association of dyslipidemia with single nucleotide polymorphisms of the cholesteryl ester transfer protein gene and cardiovascular disease risk factors in a highly admixed population. Clin Nutr ESPEN 2023; 58:242-252. [PMID: 38057013 DOI: 10.1016/j.clnesp.2023.10.002] [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: 10/04/2022] [Revised: 10/04/2023] [Accepted: 10/04/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND AND AIMS Cardiovascular diseases (CVD) are major causes of mortality worldwide, leading to premature deaths, loss of quality of life, and extensive socioeconomic impacts. Alterations in normal plasma lipid concentrations comprise important risk factors associated with CVD due to mechanisms involved in the pathophysiology of atherosclerosis. Genetic markers such as single nucleotide polymorphisms (SNPs) are known to be associated with lipid metabolism, including variants in the cholesteryl ester transfer protein (CETP) gene. Thus, the study's objective was to assess the relationship among lipid profile, socioeconomic and demographic characteristics, health status, inflammatory biomarkers, and CETP genetic variants in individuals living in a highly admixed population. METHODS The study comprises an analysis of observational cross-sectional data representative at the population level from a highly admixed population, encompassing 901 individuals from three age groups (adolescents, adults, and older adults). Socioeconomic, demographic, health, and lifestyle characteristics were collected using semi-structured questionnaires. In addition, biochemical markers and lipid profiles were obtained from individuals' blood samples. After DNA extraction, genotyping, and quality control according to Affymetrix's guidelines, information on 15 SNPs in the CETP gene was available for 707 individuals. Lipid profile and CVD risk factors were evaluated by principal component analysis (PCA), and associations between lipid traits and those factors were assessed through multiple linear regression and logistic regression. RESULTS There were low linear correlations between lipid profile and other individuals' characteristics. Two principal components were responsible for 80.8 % of the total variance, and there were minor differences in lipid profiles among individuals in different age groups. Non-HDL-c, total cholesterol, and LDL-c had the highest loadings in the first PC, and triacylglycerols, VLDL-c and HDL-c were responsible for a major part of the loading in the second PC;, whilst HDL-c and LDL-c/HDL-c ratio were significant in the third PC. In addition, there were minor differences between groups of individuals with or without dyslipidemia regarding inflammatory biomarkers (IL-1β, IL- 6, IL-10, TNF-α, CRP, and MCP-1). Being overweight, insulin resistance, and lifestyle characteristics (calories from solid fat, added sugar, alcohol and sodium, leisure physical activity, and smoking) were strong predictors of lipid traits, especially HDL-c and dyslipidemia (p < 0.05). The CETP SNPs rs7499892 and rs12691052, rs291044, and rs80180245 were significantly associated with HDL-c (p < 0.05), and their inclusion in the multiple linear regression model increased its accuracy (adjusted R2 rose from 0.12 to 0.18). CONCLUSION This study identified correlations between lipid traits and other CVD risk factors. In addition, similar lipid and inflammatory profiles across age groups in the population suggested that adolescents might already present a significant risk for developing cardiovascular diseases in the population. The risk can be primarily attributed to decreased HDL-c concentrations, which appear to be influenced by genetic factors, as evidenced by associations between SNPs in the CETP gene and HDL-c concentrations, as well as potential gene-diet interactions. Our findings underscore the significant impact of genetic and lifestyle factors on lipid profile within admixed populations in developing countries.
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Affiliation(s)
- Jean Michel R S Leite
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil.
| | - Jaqueline L Pereira
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Nágila R T Damasceno
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Júlia M Pavan Soler
- Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| | - Regina M Fisberg
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Marcelo M Rogero
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Flavia M Sarti
- School of Arts, Sciences and Humanities, University of São Paulo, Brazil
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Guo H, Wang M, Ye Y, Huang C, Wang S, Peng H, Wang X, Fan M, Hou T, Wu X, Huang X, Yan Y, Zheng K, Wu T, Li L. Short-Term Exposure to Nitrogen Dioxide Modifies Genetic Predisposition in Blood Lipid and Fasting Plasma Glucose: A Pedigree-Based Study. BIOLOGY 2023; 12:1470. [PMID: 38132296 PMCID: PMC10740487 DOI: 10.3390/biology12121470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 11/13/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023]
Abstract
(1) Background: Previous studies suggest that exposure to nitrogen dioxide (NO2) has a negative impact on health. But few studies have explored the association between NO2 and blood lipids or fasting plasma glucose (FPG), as well as gene-air pollution interactions. This study aims to fill this knowledge gap based on a pedigree cohort in southern China. (2) Methods: Employing a pedigree-based design, 1563 individuals from 452 families participated in this study. Serum levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), high-density lipoprotein cholesterol (HDLC), and FPG were measured. We investigated the associations between short-term NO2 exposure and lipid profiles or FPG using linear mixed regression models. The genotype-environment interaction (GenoXE) for each trait was estimated using variance component models. (3) Results: NO2 was inversely associated with HDLC but directly associated with TG and FPG. The results showed that each 1 μg/m3 increase in NO2 on day lag0 corresponded to a 1.926% (95%CI: 1.428-2.421%) decrease in HDLC and a 1.400% (95%CI: 0.341-2.470%) increase in FPG. Moreover, we observed a significant genotype-NO2 interaction with HDLC and FPG. (4) Conclusion: This study highlighted the association between NO2 exposure and blood lipid profiles or FPG. Additionally, our investigation suggested the presence of genotype-NO2 interactions in HDLC and FPG, indicating potential loci-specific interaction effects. These findings have the potential to inform and enhance the interpretation of studies that are focused on specific gene-environment interactions.
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Affiliation(s)
- Huangda Guo
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Mengying Wang
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Department of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing 100191, China
| | - Ying Ye
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China
| | - Chunlan Huang
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing 363600, China
| | - Siyue Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Hexiang Peng
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Xueheng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Meng Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Tianjiao Hou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Xiaoling Wu
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing 363600, China
| | - Xiaoming Huang
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing 363600, China
| | - Yansheng Yan
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China
| | - Kuicheng Zheng
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China
| | - Tao Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Key Laboratory of Reproductive Health, Ministry of Health, Beijing 100191, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing 100191, China
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7
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Beyene HB, Giles C, Huynh K, Wang T, Cinel M, Mellett NA, Olshansky G, Meikle TG, Watts GF, Hung J, Hui J, Cadby G, Beilby J, Blangero J, Moses EK, Shaw JE, Magliano DJ, Meikle PJ. Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts. Nat Commun 2023; 14:6280. [PMID: 37805498 PMCID: PMC10560260 DOI: 10.1038/s41467-023-41963-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 09/26/2023] [Indexed: 10/09/2023] Open
Abstract
Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of "metabolically healthy obese". We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify "at risk" individuals for targeted intervention and monitoring.
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Affiliation(s)
- Habtamu B Beyene
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Michelle Cinel
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | | | | | - Thomas G Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
| | - Gerald F Watts
- School of Medicine, University of Western Australia, Perth, WA, Australia
- Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, WA, Australia
| | - Joseph Hung
- School of Medicine, University of Western Australia, Perth, WA, Australia
| | - Jennie Hui
- PathWest Laboratory Medicine of Western Australia, Nedlands, WA, Australia
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
- School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
| | - Gemma Cadby
- School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
| | - John Beilby
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric K Moses
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Dianna J Magliano
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia.
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia.
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Jurrjens AW, Seldin MM, Giles C, Meikle PJ, Drew BG, Calkin AC. The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases. eLife 2023; 12:e86139. [PMID: 37000167 PMCID: PMC10065800 DOI: 10.7554/elife.86139] [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: 01/16/2023] [Accepted: 03/15/2023] [Indexed: 04/01/2023] Open
Abstract
Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.
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Affiliation(s)
- Aaron W Jurrjens
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
| | - Marcus M Seldin
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, United States
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Brian G Drew
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
| | - Anna C Calkin
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
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9
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Shao C, Xu L, Lei P, Wang W, Feng S, Ye J, Zhong B. Metabolomics to identify fingerprints of carotid atherosclerosis in nonobese metabolic dysfunction-associated fatty liver disease. J Transl Med 2023; 21:12. [PMID: 36624524 PMCID: PMC9830861 DOI: 10.1186/s12967-022-03760-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/06/2022] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND/AIMS Nonobese metabolic dysfunction-associated fatty liver disease (MAFLD) is paradoxically associated with improved metabolic and pathological features at diagnosis but similar cardiovascular diseases (CVD) prognosis to obese MAFLD. We aimed to utilize the metabolomics to identify the potential metabolite profiles accounting for this phenomenon. METHODS This prospective multicenter cross-sectional study was conducted in China enrolling derivation and validation cohorts. Liquid chromatography coupled with mass spectrometry and gas chromatography-mass spectrometry were applied to perform a metabolomics measurement. RESULTS The study involved 120 MAFLD patients and 60 non-MAFLD controls in the derivation cohort. Controls were divided into two groups according to the presence of carotid atherosclerosis (CAS). The MAFLD group was further divided into nonobese MAFLD with/without CAS groups and obese MAFLD with/without CAS groups. Fifty-six metabolites were statistically significant for discriminating the six groups. Among the top 10 metabolites related to CAS in nonobese MAFLD, only phosphatidylethanolamine (PE 20:2/16:0), phosphatidylglycerol (PG 18:0/20:4) and de novo lipogenesis (16:0/18:2n-6) achieved significant areas under the ROC curve (AUCs, 0.67, p = 0.03; 0.79, p = 0.02; 0.63, p = 0.03, respectively). The combination of these three metabolites and liver stiffness achieved a significantly higher AUC (0.92, p < 0.01). In obese MAFLD patients, cystine was found to be significant with an AUC of 0.69 (p = 0.015), followed by sphingomyelin (SM 16:1/18:1) (0.71, p = 0.004) and de novo lipogenesis (16:0/18:2n-6) (0.73, p = 0.004). The combination of these three metabolites, liver fat content and age attained a significantly higher AUC of 0.91 (p < 0.001). The AUCs of these metabolites remained highly significant in the independent validation cohorts involving 200 MAFLD patients and 90 controls. CONCLUSIONS Diagnostic models combining different metabolites according to BMI categories could raise the accuracy of identifying subclinical CAS. Trial registration The study protocol was approved by the local ethics committee and all the participants have provided written informed consent (Approval number: [2014] No. 112, registered at the Chinese Clinical Trial Registry, ChiCTR-ChiCTR2000034197).
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Affiliation(s)
- Congxiang Shao
- grid.12981.330000 0001 2360 039XDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan II Road, Yuexiu District, Guangzhou, 510080 China
| | - Lishu Xu
- grid.410643.4Department of Gastroenterology and Hepatology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, and Guangdong Provincial Geriatrics Institute, No. 106 Zhongshan II Road, Yuexiu District, Guangzhou, China
| | - Pingguang Lei
- Department of Gastroenterology, Shenzhen Baoan District Songgang People’s Hospital, No. 2, Shajiang Road, Songgang Street, Bao’an District, Shenzhen, China
| | - Wei Wang
- grid.12981.330000 0001 2360 039XDepartment of Medical Ultrasonics, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan II Road, Yuexiu District, Guangzhou, China
| | - Shiting Feng
- grid.12981.330000 0001 2360 039XDepartment of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan II Road, Yuexiu District, Guangzhou, China
| | - Junzhao Ye
- grid.12981.330000 0001 2360 039XDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan II Road, Yuexiu District, Guangzhou, 510080 China
| | - Bihui Zhong
- grid.12981.330000 0001 2360 039XDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan II Road, Yuexiu District, Guangzhou, 510080 China
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Belkin TG, Tham YK, McMullen JR. Lipids regulated by exercise and PI3K: potential role as biomarkers and therapeutic targets for cardiovascular disease. CURRENT OPINION IN PHYSIOLOGY 2023. [DOI: 10.1016/j.cophys.2023.100633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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11
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Exploring the Genetic Association between Obesity and Serum Lipid Levels Using Bivariate Methods. Twin Res Hum Genet 2022; 25:234-244. [PMID: 36606461 DOI: 10.1017/thg.2022.39] [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] [Indexed: 01/07/2023]
Abstract
It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.
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Wang T, Huynh K, Giles C, Mellett NA, Duong T, Nguyen A, Lim WLF, Smith AAT, Olshansky G, Cadby G, Hung J, Hui J, Beilby J, Watts GF, Chatterjee P, Martins I, Laws SM, Bush AI, Rowe CC, Villemagne VL, Ames D, Masters CL, Taddei K, Doré V, Fripp J, Arnold M, Kastenmüller G, Nho K, Saykin AJ, Baillie R, Han X, Martins RN, Moses EK, Kaddurah‐Daouk R, Meikle PJ. APOE ε2 resilience for Alzheimer's disease is mediated by plasma lipid species: Analysis of three independent cohort studies. Alzheimers Dement 2022; 18:2151-2166. [PMID: 35077012 PMCID: PMC9787288 DOI: 10.1002/alz.12538] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 10/19/2021] [Accepted: 10/25/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The apolipoprotein E (APOE) genotype is the strongest genetic risk factor for late-onset Alzheimer's disease. However, its effect on lipid metabolic pathways, and their mediating effect on disease risk, is poorly understood. METHODS We performed lipidomic analysis on three independent cohorts (the Australian Imaging, Biomarkers and Lifestyle [AIBL] flagship study, n = 1087; the Alzheimer's Disease Neuroimaging Initiative [ADNI] 1 study, n = 819; and the Busselton Health Study [BHS], n = 4384), and we defined associations between APOE ε2 and ε4 and 569 plasma/serum lipid species. Mediation analysis defined the proportion of the treatment effect of the APOE genotype mediated by plasma/serum lipid species. RESULTS A total of 237 and 104 lipid species were associated with APOE ε2 and ε4, respectively. Of these 68 (ε2) and 24 (ε4) were associated with prevalent Alzheimer's disease. Individual lipid species or lipidomic models of APOE genotypes mediated up to 30% and 10% of APOE ε2 and ε4 treatment effect, respectively. DISCUSSION Plasma lipid species mediate the treatment effect of APOE genotypes on Alzheimer's disease and as such represent a potential therapeutic target.
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Selvaraj MS, Li X, Li Z, Pampana A, Zhang DY, Park J, Aslibekyan S, Bis JC, Brody JA, Cade BE, Chuang LM, Chung RH, Curran JE, de Las Fuentes L, de Vries PS, Duggirala R, Freedman BI, Graff M, Guo X, Heard-Costa N, Hidalgo B, Hwu CM, Irvin MR, Kelly TN, Kral BG, Lange L, Li X, Lisa M, Lubitz SA, Manichaikul AW, Michael P, Montasser ME, Morrison AC, Naseri T, O'Connell JR, Palmer ND, Peyser PA, Reupena MS, Smith JA, Sun X, Taylor KD, Tracy RP, Tsai MY, Wang Z, Wang Y, Bao W, Wilkins JT, Yanek LR, Zhao W, Arnett DK, Blangero J, Boerwinkle E, Bowden DW, Chen YDI, Correa A, Cupples LA, Dutcher SK, Ellinor PT, Fornage M, Gabriel S, Germer S, Gibbs R, He J, Kaplan RC, Kardia SLR, Kim R, Kooperberg C, Loos RJF, Viaud-Martinez KA, Mathias RA, McGarvey ST, Mitchell BD, Nickerson D, North KE, Psaty BM, Redline S, Reiner AP, Vasan RS, Rich SS, Willer C, Rotter JI, Rader DJ, Lin X, Peloso GM, Natarajan P. Whole genome sequence analysis of blood lipid levels in >66,000 individuals. Nat Commun 2022; 13:5995. [PMID: 36220816 PMCID: PMC9553944 DOI: 10.1038/s41467-022-33510-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 09/21/2022] [Indexed: 01/05/2023] Open
Abstract
Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.
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Affiliation(s)
- Margaret Sunitha Selvaraj
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02114, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Akhil Pampana
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - David Y Zhang
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joseph Park
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Brian E Cade
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lee-Ming Chuang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Ren-Hua Chung
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, 350, Taiwan
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA
| | - Lisa de Las Fuentes
- Department of Medicine, Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ravindranath Duggirala
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA
| | - Barry I Freedman
- Department of Internal Medicine, Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Mariaelisa Graff
- Department of Epidemiology, UNC Chapel Hill, Chapel Hill, NC, 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
| | - Nancy Heard-Costa
- Department of Neurology, Boston university School of Medicine, Boston, MA, USA
| | - Bertha Hidalgo
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Tanika N Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA
- Tulane University Translational Science Institute, New Orleans, LA, 70112, USA
| | - Brian G Kral
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Leslie Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Xiaohui Li
- 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
| | - Martin Lisa
- Department of Medicine, George Washington University, Washingron, DC, USA
| | - Steven A Lubitz
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02114, USA
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
| | - Ani W Manichaikul
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Preuss Michael
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - May E Montasser
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Take Naseri
- Ministry of Health, Government of Samoa, Samoa, USA
| | - Jeffrey R O'Connell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - Jennifer A Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, 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
| | - Russell P Tracy
- Departments of Pathology & Laboratory Medicine and Biochemistry, Larner College of Medicine at the University of Vermont, Colchester, VT, USA
| | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minneosta, Minneapolis, MN, USA
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuxuan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Wei Bao
- Institute of Public Health, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - John T Wilkins
- Department of Medicine (Cardiology) and Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Wei Zhao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Donna K Arnett
- Dean's Office, University of Kentucky College of Public Health, Lexington, KY, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Yii-Der Ida Chen
- Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Adolfo Correa
- Department of Population Health Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Susan K Dutcher
- The McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02114, USA
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 7722, USA
| | | | - Soren Germer
- New York Genome Center, New York, NY, 10013, USA
| | - Richard Gibbs
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, 77030, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA
- Tulane University Translational Science Institute, New Orleans, LA, 70112, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ryan Kim
- Psomagen, Inc. (formerly Macrogen USA), Rockville, MD, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- NNF Center for Basic Metabolic Research, University of Copenhagen, Cophenhagen, Denmark
| | | | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Stephen T McGarvey
- Department of Epidemiology, International Health Institute, Brown University, Providence, RI, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - Deborah Nickerson
- University of Washington, Department of Genome Sciences, Seattle, WA, 98195, USA
| | - Kari E North
- Department of Epidemiology, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Ramachandran S Vasan
- Sections of Preventive medicine and Epidemiology, Cardiovascular medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Stephen S Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Cristen Willer
- University of Michigan, Internal Medicine, Ann Arbor, MI, 48109, 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
| | - Daniel J Rader
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xihong Lin
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Statistics, Harvard University, Cambridge, MA, 02138, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA.
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02114, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA.
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Leandro AC, Michael LF, Almeida M, Kuokkanen M, Huynh K, Giles C, Duong T, Diego VP, Duggirala R, Clarke GD, Blangero J, Meikle PJ, Curran JE. Influence of the Human Lipidome on Epicardial Fat Volume in Mexican American Individuals. Front Cardiovasc Med 2022; 9:889985. [PMID: 35734277 PMCID: PMC9207321 DOI: 10.3389/fcvm.2022.889985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/05/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Cardiovascular disease (CVD) is the leading cause of mortality worldwide and is the leading cause of death in the US. Lipid dysregulation is a well-known precursor to metabolic diseases, including CVD. There is a growing body of literature that suggests MRI-derived epicardial fat volume, or epicardial adipose tissue (EAT) volume, is linked to the development of coronary artery disease. Interestingly, epicardial fat is also actively involved in lipid and energy homeostasis, with epicardial adipose tissue having a greater capacity for release and uptake of free fatty acids. However, there is a scarcity of knowledge on the influence of plasma lipids on EAT volume. Aim The focus of this study is on the identification of novel lipidomic species associated with CMRI-derived measures of epicardial fat in Mexican American individuals. Methods We performed lipidomic profiling on 200 Mexican American individuals. High-throughput mass spectrometry enabled rapid capture of precise lipidomic profiles, providing measures of 799 unique species from circulating plasma samples. Because of our extended pedigree design, we utilized a standard quantitative genetic linear mixed model analysis to determine whether lipids were correlated with EAT by formally testing for association between each lipid species and the CMRI epicardial fat phenotype. Results After correction for multiple testing using the FDR approach, we identified 135 lipid species showing significant association with epicardial fat. Of those, 131 lipid species were positively correlated with EAT, where increased circulating lipid levels were correlated with increased epicardial fat. Interestingly, the top 10 lipid species associated with an increased epicardial fat volume were from the deoxyceramide (Cer(m)) and triacylglycerol (TG) families. Deoxyceramides are atypical and neurotoxic sphingolipids. Triacylglycerols are an abundant lipid class and comprise the bulk of storage fat in tissues. Pathologically elevated TG and Cer(m) levels are related to CVD risk and, in our study, to EAT volume. Conclusion Our results indicate that specific lipid abnormalities such as enriched saturated triacylglycerols and the presence of toxic ceramides Cer(m) in plasma of our individuals could precede CVD with increased EAT volume.
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Affiliation(s)
- Ana Cristina Leandro
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, United States
| | | | - Marcio Almeida
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, United States
| | - Mikko Kuokkanen
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, United States
| | - Kevin Huynh
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, VIC, Australia
| | - Corey Giles
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, VIC, Australia
| | - Thy Duong
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Vincent P. Diego
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, United States
| | - Ravindranath Duggirala
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, United States
| | - Geoffrey D. Clarke
- Department of Radiology and Research Imaging Institute, University of Texas Health Science Center, San Antonio, TX, United States
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, United States
| | - Peter J. Meikle
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, VIC, Australia
| | - Joanne E. Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, United States
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15
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Cadby G, Giles C, Melton PE, Huynh K, Mellett NA, Duong T, Nguyen A, Cinel M, Smith A, Olshansky G, Wang T, Brozynska M, Inouye M, McCarthy NS, Ariff A, Hung J, Hui J, Beilby J, Dubé MP, Watts GF, Shah S, Wray NR, Lim WLF, Chatterjee P, Martins I, Laws SM, Porter T, Vacher M, Bush AI, Rowe CC, Villemagne VL, Ames D, Masters CL, Taddei K, Arnold M, Kastenmüller G, Nho K, Saykin AJ, Han X, Kaddurah-Daouk R, Martins RN, Blangero J, Meikle PJ, Moses EK. Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease. Nat Commun 2022; 13:3124. [PMID: 35668104 PMCID: PMC9170690 DOI: 10.1038/s41467-022-30875-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 05/17/2022] [Indexed: 12/26/2022] Open
Abstract
We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species putatively in the mechanistic pathway for coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 individuals from the Busselton Health Study. The discovery GWAS identified 3,361 independent lipid-loci associations, involving 667 genomic regions (479 previously unreported), with validation in two independent cohorts. A meta-analysis revealed an additional 70 independent genomic regions associated with lipid species. We identified 134 lipid endophenotypes for CAD associated with 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P < 1 × 10-3), 43 loci were associated with at least one lipid endophenotype. These findings illustrate the value of integrative biology to investigate the aetiology of atherosclerosis and CAD, with implications for other complex diseases.
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Affiliation(s)
- Gemma Cadby
- School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia
| | - Phillip E Melton
- School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
- Menzies Research Institute, University of Tasmania, Hobart, TAS, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia
| | | | - Thy Duong
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Anh Nguyen
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Michelle Cinel
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Alex Smith
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Gavriel Olshansky
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia
| | - Marta Brozynska
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Mike Inouye
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Nina S McCarthy
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
| | - Amir Ariff
- School of Women's and Children's Health, University of New South Wales, Sydney, NSW, Australia
| | - Joseph Hung
- School of Medicine, The University of Western Australia, Crawley, WA, Australia
- Department of Cardiovascular Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
- Busselton Population Medical Research Institute Inc., Perth, WA, Australia
| | - Jennie Hui
- Busselton Population Medical Research Institute Inc., Perth, WA, Australia
- PathWest Laboratory Medicine WA, Perth, WA, Australia
| | - John Beilby
- Busselton Population Medical Research Institute Inc., Perth, WA, Australia
- PathWest Laboratory Medicine WA, Perth, WA, Australia
| | - Marie-Pierre Dubé
- Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal Heart Institute, Montreal, QC, Canada
| | - Gerald F Watts
- School of Medicine, The University of Western Australia, Crawley, WA, Australia
- Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, WA, Australia
| | - Sonia Shah
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Wei Ling Florence Lim
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Cooperative research Centre (CRC) for Mental Health, Joondalup, WA, Australia
| | - Pratishtha Chatterjee
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, Australia
- KaRa Institute of Neurological Disease, Sydney, Macquarie Park, NSW, Australia
| | - Ian Martins
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Simon M Laws
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia
- Collaborative Genomics Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia
| | - Tenielle Porter
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia
- Collaborative Genomics Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia
| | - Michael Vacher
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia
- Collaborative Genomics Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- The Australian e-Health Research Centre, Health and Biosecurity, CSIRO, Floreat, WA, Australia
| | - Ashley I Bush
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Christopher C Rowe
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, VIC, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, VIC, Australia
- Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC, Australia
| | - David Ames
- National Ageing Research Institute, Parkville, VIC, Australia
- University of Melbourne Academic Unit for Psychiatry of Old Age, St George's Hospital, Kew, VIC, Australia
| | - Colin L Masters
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Kevin Taddei
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Xianlin Han
- Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Ralph N Martins
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Cooperative research Centre (CRC) for Mental Health, Joondalup, WA, Australia
- Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, Australia
- KaRa Institute of Neurological Disease, Sydney, Macquarie Park, NSW, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia.
- Monash University, Melbourne, VIC, Australia.
| | - Eric K Moses
- Menzies Research Institute, University of Tasmania, Hobart, TAS, Australia.
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia.
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16
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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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17
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Uitterlinden AG. Diversity in human genetics studies accelerates discovery and improves health care. Nat Rev Cardiol 2022; 19:289-290. [PMID: 35314811 DOI: 10.1038/s41569-022-00696-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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18
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Gao F, Tom E, Skowronska-Krawczyk D. Dynamic Progress in Technological Advances to Study Lipids in Aging: Challenges and Future Directions. FRONTIERS IN AGING 2022; 3:851073. [PMID: 35821837 PMCID: PMC9261449 DOI: 10.3389/fragi.2022.851073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 02/23/2022] [Indexed: 11/29/2022]
Abstract
Lipids participate in all cellular processes. Diverse methods have been developed to investigate lipid composition and distribution in biological samples to understand the effect of lipids across an organism’s lifespan. Here, we summarize the advanced techniques for studying lipids, including mass spectrometry-based lipidomics, lipid imaging, chemical-based lipid analysis and lipid engineering and their advantages. We further discuss the limitation of the current methods to gain an in-depth knowledge of the role of lipids in aging, and the possibility of lipid-based therapy in aging-related diseases.
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Affiliation(s)
- Fangyuan Gao
- Department of Ophthalmology, Center for Translational Vision Research, School of Medicine, UC Irvine, Irvine, CA, United States
| | - Emily Tom
- Department of Physiology and Biophysics, Department of Ophthalmology, Center for Translational Vision Research, School of Medicine, UC Irvine, Irvine, CA, United States
| | - Dorota Skowronska-Krawczyk
- Department of Ophthalmology, Center for Translational Vision Research, School of Medicine, UC Irvine, Irvine, CA, United States
- Department of Physiology and Biophysics, Department of Ophthalmology, Center for Translational Vision Research, School of Medicine, UC Irvine, Irvine, CA, United States
- *Correspondence: Dorota Skowronska-Krawczyk,
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19
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Ceramides: Shared Lipid Biomarkers of Cardiovascular Disease and Schizophrenia. CONSORTIUM PSYCHIATRICUM 2021. [DOI: 10.17816/cp101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
INTRODUCTION: Schizophrenia, although a debilitating mental illness, greatly affects individuals physical health as well. One of the leading somatic comorbidities associated with schizophrenia is cardiovascular disease, which has been estimated to be one of the leading causes of excess mortality in patients diagnosed with schizophrenia. Although the shared susceptibility to schizophrenia and cardiovascular disease is well established, the mechanisms linking these two disorders are not well understood. Genetic studies have hinted toward shared lipid metabolism abnormalities co-occurring in the two disorders, while lipid compounds have emerged as prognostic markers for cardiovascular disease. In particular, three ceramide species in the blood plasma, Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1), have been robustly linked to the latter disorder.
AIM: We aimed to assess the differences in abundances of Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) in the blood plasma of schizophrenia patients compared to healthy controls.
METHODS: We measured the abundances of Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) in a cohort of 82 patients with schizophrenia and 138 controls without a psychiatric diagnosis and validated the results using an independent cohort of 26 patients with schizophrenia, 55 control individuals, and 19 patients experiencing a first psychotic episode.
RESULTS: We found significant alterations for all three ceramide species Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) and a particularly strong difference in concentrations between psychiatric patients and controls for the ceramide species Cer(d18:1/18:0).
CONCLUSIONS: The alteration of Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) levels in the blood plasma might be a manifestation of metabolic abnormalities common to both schizophrenia and cardiovascular disease.
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20
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Awany D, Allali I, Chimusa ER. Dissecting genome-wide studies for microbiome-related metabolic diseases. Hum Mol Genet 2021; 29:R73-R80. [PMID: 32478833 DOI: 10.1093/hmg/ddaa105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 05/14/2020] [Accepted: 05/29/2020] [Indexed: 12/14/2022] Open
Abstract
Despite the meteoric rise in genome-wide association studies for metabolic diseases (MetD) over the last few years, our understanding of the pathogenesis of these diseases is still far from complete. Recent developments have established that MetD arises from complex interactions between host genetics, the gut microbiome and the environment. However, our knowledge of the genetic and microbiome components involved and the underlying molecular mechanisms remains limited. Here, we review and summarize recent studies investigating the genetic and microbiome basis of MetD. Then, given the critical importance of study-individual's ancestry in these studies, we leverage 4932 whole-genome sequence samples from 18 worldwide ethnic groups to examine genetic diversity in currently reported variants associated with MetD. The analyses show marked differences in gene-specific proportion of pathogenic single-nucleotide polymorphisms (SNPs) and gene-specific SNPs MAFs across ethnic groups, highlighting the importance of population- and ethnic-specific investigations in pinpointing the causative factors for MetD. We conclude with a discussion of research areas where further investigation on interactions between host genetics, microbiome and the environment is needed.
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Affiliation(s)
- Denis Awany
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, Cape Town, South Africa
| | - Imane Allali
- Laboratory of Human Pathologies Biology, Department of Biology, Faculty of Sciences, and Genomic Center of Human Pathologies, Faculty of Medicine and Pharmacy, Mohammed V University, Agdal Rabat, B.P, 8007 N.U, Morocco
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, Cape Town, South Africa.,Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory 7925, Cape Town, South Africa
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21
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McGurk KA, Keavney BD, Nicolaou A. Circulating ceramides as biomarkers of cardiovascular disease: Evidence from phenotypic and genomic studies. Atherosclerosis 2021; 327:18-30. [PMID: 34004484 DOI: 10.1016/j.atherosclerosis.2021.04.021] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 04/25/2021] [Accepted: 04/30/2021] [Indexed: 12/20/2022]
Abstract
There is a need for new biomarkers of atherosclerotic cardiovascular disease (ACVD), the main cause of death globally. Ceramides, a class of potent bioactive lipid mediators, have signalling roles in apoptosis, cellular stress and inflammation. Recent studies have highlighted circulating ceramides as novel biomarkers of coronary artery disease, type-2 diabetes and insulin resistance. Ceramides are highly regulated by enzymatic reactions throughout the body in terms of their activity and metabolism, including production, degradation and transport. The genetic studies that have been completed to date on the main ceramide species found in circulation are described, highlighting the importance of DNA variants in genes involved in ceramide biosynthesis as key influencers of heritable, circulating ceramide levels. We also review studies of disease associations with ceramides and discuss mechanistic insights deriving from recent genomic studies. The signalling activities of ceramides in vascular inflammation and apoptosis, associations between circulating ceramides and coronary artery disease risk, type-2 diabetes and insulin resistance, and the potential importance of ceramides with regard to ACVD risk factors, such as blood pressure, lipoproteins and lifestyle factors, are also discussed.
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Affiliation(s)
- Kathryn A McGurk
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, UK; Laboratory for Lipidomics and Lipid Research, Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, UK
| | - Bernard D Keavney
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, UK; Manchester Heart Centre, Manchester University NHS Foundation Trust, UK
| | - Anna Nicolaou
- Laboratory for Lipidomics and Lipid Research, Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, UK; Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
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22
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McGurk KA, Williams SG, Guo H, Watkins H, Farrall M, Cordell HJ, Nicolaou A, Keavney BD. Heritability and family-based GWAS analyses of the N-acyl ethanolamine and ceramide plasma lipidome. Hum Mol Genet 2021; 30:500-513. [PMID: 33437986 PMCID: PMC8101358 DOI: 10.1093/hmg/ddab002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/25/2020] [Accepted: 12/23/2020] [Indexed: 12/11/2022] Open
Abstract
Signalling lipids of the N-acyl ethanolamine (NAE) and ceramide (CER) classes have emerged as potential biomarkers of cardiovascular disease (CVD). We sought to establish the heritability of plasma NAEs (including the endocannabinoid anandamide) and CERs, to identify common DNA variants influencing the circulating concentrations of the heritable lipids, and assess causality of these lipids in CVD using 2-sample Mendelian randomization (2SMR). Nine NAEs and 16 CERs were analyzed in plasma samples from 999 members of 196 British Caucasian families, using targeted ultra-performance liquid chromatography with tandem mass spectrometry. All lipids were significantly heritable (h2 = 36-62%). A missense variant (rs324420) in the gene encoding the enzyme fatty acid amide hydrolase (FAAH), which degrades NAEs, associated at genome-wide association study (GWAS) significance (P < 5 × 10-8) with four NAEs (DHEA, PEA, LEA and VEA). For CERs, rs680379 in the SPTLC3 gene, which encodes a subunit of the rate-limiting enzyme in CER biosynthesis, associated with a range of species (e.g. CER[N(24)S(19)]; P = 4.82 × 10-27). We observed three novel associations between SNPs at the CD83, SGPP1 and DEGS1 loci, and plasma CER traits (P < 5 × 10-8). 2SMR in the CARDIoGRAMplusC4D cohorts (60 801 cases; 123 504 controls) and in the DIAGRAM cohort (26 488 cases; 83 964 controls), using the genetic instruments from our family-based GWAS, did not reveal association between genetically determined differences in CER levels and CVD or diabetes. Two of the novel GWAS loci, SGPP1 and DEGS1, suggested a casual association between CERs and a range of haematological phenotypes, through 2SMR in the UK Biobank, INTERVAL and UKBiLEVE cohorts (n = 110 000-350 000).
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Affiliation(s)
- Kathryn A McGurk
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9NT, UK
- Laboratory for Lipidomics and Lipid Biology, Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PG, UK
| | - Simon G Williams
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9NT, UK
| | - Hui Guo
- Division of Population Health, Health Services Research & Primary Care, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PL, UK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Heather J Cordell
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Anna Nicolaou
- Laboratory for Lipidomics and Lipid Biology, Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PG, UK
| | - Bernard D Keavney
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9NT, UK
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
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23
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Wang Z, Chen H, Qin M, Liu C, Ma Q, Chen X, Zhang Y, Lai W, Zhang X, Zhong S. Associations of Mitochondrial Variants With Lipidomic Traits in a Chinese Cohort With Coronary Artery Disease. Front Genet 2021; 12:630359. [PMID: 33841498 PMCID: PMC8027325 DOI: 10.3389/fgene.2021.630359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/08/2021] [Indexed: 11/13/2022] Open
Abstract
Plasma lipids have been at the center stage of the prediction and prevention strategies for cardiovascular diseases (CVDs), and novel lipidomic traits have been recognized as reliable biomarkers for CVD risk prediction. The mitochondria serve as energy supply sites for cells and can synthesize a variety of lipids autonomously. Therefore, investigating the relationships between mitochondrial single nucleotide polymorphism (SNPs) and plasma lipidomic traits is meaningful. Here, we enrolled a total of 1,409 Han Chinese patients with coronary artery disease from three centers and performed linear regression analyses on the SNPs of mitochondrial DNA (mtDNA) and lipidomic traits in two independent groups. Sex, age, aspartate aminotransferase, estimated glomerular filtration rate, antihypertensive drugs, hypertension, and diabetes were adjusted. We identified three associations, namely, D-loopm.16089T>C with TG(50:4) NL-16:0, D-loopm.16145G>A with TG(54:5) NL-18:0, and D-loopm.16089T>C with PC(16:0_16:1) at the statistically significant threshold of FDR < 0.05. Then, we explored the relationships between mitochondrial genetic variants and traditional lipids, including triglyceride, total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), and high-density lipoprotein cholesterol. Two significant associations were found, namely MT-ND6m.14178T>C with TC and D-loopm.215A>G with LDLC. Furthermore, we performed linear regression analysis to determine on the SNPs of mtDNA and left ventricular ejection fraction (LVEF) and found that the SNP D-loopm.16145G>A was nominally significantly associated with LVEF (P = 0.047). Our findings provide insights into the lipidomic context of mtDNA variations and highlight the importance of studying mitochondrial genetic variants related to lipid species.
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Affiliation(s)
- Zixian Wang
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Hui Chen
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Min Qin
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chen Liu
- Department of Cardiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qilin Ma
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaoping Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
| | - Ying Zhang
- Department of Cardiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Weihua Lai
- Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xiaojuan Zhang
- Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Shilong Zhong
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China.,Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
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24
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Tabassum R, Ripatti S. Integrating lipidomics and genomics: emerging tools to understand cardiovascular diseases. Cell Mol Life Sci 2021; 78:2565-2584. [PMID: 33449144 PMCID: PMC8004487 DOI: 10.1007/s00018-020-03715-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/09/2020] [Accepted: 11/16/2020] [Indexed: 02/07/2023]
Abstract
Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity worldwide leading to 31% of all global deaths. Early prediction and prevention could greatly reduce the enormous socio-economic burden posed by CVDs. Plasma lipids have been at the center stage of the prediction and prevention strategies for CVDs that have mostly relied on traditional lipids (total cholesterol, total triglycerides, HDL-C and LDL-C). The tremendous advancement in the field of lipidomics in last two decades has facilitated the research efforts to unravel the metabolic dysregulation in CVDs and their genetic determinants, enabling the understanding of pathophysiological mechanisms and identification of predictive biomarkers, beyond traditional lipids. This review presents an overview of the application of lipidomics in epidemiological and genetic studies and their contributions to the current understanding of the field. We review findings of these studies and discuss examples that demonstrates the potential of lipidomics in revealing new biology not captured by traditional lipids and lipoprotein measurements. The promising findings from these studies have raised new opportunities in the fields of personalized and predictive medicine for CVDs. The review further discusses prospects of integrating emerging genomics tools with the high-dimensional lipidome to move forward from the statistical associations towards biological understanding, therapeutic target development and risk prediction. We believe that integrating genomics with lipidome holds a great potential but further advancements in statistical and computational tools are needed to handle the high-dimensional and correlated lipidome.
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Affiliation(s)
- Rubina Tabassum
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, PO Box 20, 00014, Helsinki, Finland.
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, PO Box 20, 00014, Helsinki, Finland.
- Department of Public Health, Clinicum, University of Helsinki, Helsinki, Finland.
- Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
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25
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Gao L, Ji S, Burla B, Wenk MR, Torta F, Cazenave-Gassiot A. LICAR: An Application for Isotopic Correction of Targeted Lipidomic Data Acquired with Class-Based Chromatographic Separations Using Multiple Reaction Monitoring. Anal Chem 2021; 93:3163-3171. [PMID: 33535740 DOI: 10.1021/acs.analchem.0c04565] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Lipidomics is developing as an important area in biomedical and clinical research. Reliable quantification of lipid species is required for clinical translation of lipidomic studies. Hydrophilic interaction chromatography (HILIC), normal-phase liquid chromatography (NPLC), and supercritical fluid chromatography (SFC) are commonly used techniques in lipidomics and provide class-based separation of lipids. While co-elution of lipid species and their internal standards is an advantage for accurate quantification, it leads to isotopic overlap between species of the same lipid class. In shotgun lipidomics, isotopic correction is typically done based on elemental formulas of precursor ions. In multiple reaction monitoring (MRM) analyses, however, this approach should not be used, as the overall contribution of heavy isotopes to the MRM transitions' intensities depends on their location in the molecule with respect to the fragmentation pattern. We present an algorithm, provided in the R programming language, for isotopic correction in class-based separation using MRM, extracting relevant structural information from MRM transitions to apply adequate isotopic correction factors. Using standards, we show that our algorithm accurately estimates the isotopic contribution of isotopologues to MRM transitions' measured intensities. Using human plasma as an example, we demonstrate the necessity of adequate isotopic correction for accurate quantitation of lipids measured by MRM with class-based chromatographic separation. We show that over a third of the measured phosphatidylcholine species had their intensity corrected by more than 10%. This isotopic correction algorithm and R-implemented application enable a more accurate quantification of lipids in class-based separation-MRM, a prerequisite for successful translation of lipidomic applications.
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Affiliation(s)
- Liang Gao
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 28 Medical Drive #03-03, 117456 Singapore.,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, 28 Medical Drive #03-03, 117456 Singapore
| | - Shanshan Ji
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, 28 Medical Drive #03-03, 117456 Singapore
| | - Bo Burla
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, 28 Medical Drive #03-03, 117456 Singapore
| | - Markus R Wenk
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 28 Medical Drive #03-03, 117456 Singapore.,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, 28 Medical Drive #03-03, 117456 Singapore
| | - Federico Torta
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 28 Medical Drive #03-03, 117456 Singapore.,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, 28 Medical Drive #03-03, 117456 Singapore
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 28 Medical Drive #03-03, 117456 Singapore.,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, 28 Medical Drive #03-03, 117456 Singapore
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26
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Kovilakath A, Jamil M, Cowart LA. Sphingolipids in the Heart: From Cradle to Grave. Front Endocrinol (Lausanne) 2020; 11:652. [PMID: 33042014 PMCID: PMC7522163 DOI: 10.3389/fendo.2020.00652] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 08/11/2020] [Indexed: 01/10/2023] Open
Abstract
Cardiovascular diseases are the leading cause of mortality worldwide and this has largely been driven by the increase in metabolic disease in recent decades. Metabolic disease alters metabolism, distribution, and profiles of sphingolipids in multiple organs and tissues; as such, sphingolipid metabolism and signaling have been vigorously studied as contributors to metabolic pathophysiology in various pathological outcomes of obesity, including cardiovascular disease. Much experimental evidence suggests that targeting sphingolipid metabolism may be advantageous in the context of cardiometabolic disease. The heart, however, is a structurally and functionally complex organ where bioactive sphingolipids have been shown not only to mediate pathological processes, but also to contribute to essential functions in cardiogenesis and cardiac function. Additionally, some sphingolipids are protective in the context of ischemia/reperfusion injury. In addition to mechanistic contributions, untargeted lipidomics approaches used in recent years have identified some specific circulating sphingolipids as novel biomarkers in the context of cardiovascular disease. In this review, we summarize recent literature on both deleterious and beneficial contributions of sphingolipids to cardiogenesis and myocardial function as well as recent identification of novel sphingolipid biomarkers for cardiovascular disease risk prediction and diagnosis.
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Affiliation(s)
- Anna Kovilakath
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Maryam Jamil
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Lauren Ashley Cowart
- Department of Biochemistry and Molecular Biology and the Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, United States
- Hunter Holmes McGuire Veteran's Affairs Medical Center, Richmond, VA, United States
- *Correspondence: Lauren Ashley Cowart
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