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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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Shang X, Liu J, Zhu Z, Zhang X, Huang Y, Liu S, Wang W, Zhang X, Ma S, Tang S, Hu Y, Ge Z, Yu H, He M. Metabolomic age and risk of 50 chronic diseases in community-dwelling adults: A prospective cohort study. Aging Cell 2024; 23:e14125. [PMID: 38380547 PMCID: PMC11113347 DOI: 10.1111/acel.14125] [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: 07/26/2023] [Revised: 01/25/2024] [Accepted: 02/11/2024] [Indexed: 02/22/2024] Open
Abstract
It is unclear how metabolomic age is associated with the risk of a wide range of chronic diseases. Our analysis included 110,692 participants (training: n = 27,673; testing: n = 27,673; validating: n = 55,346) aged 39-71 years at baseline (2006-2010) from the UK Biobank. Incident chronic diseases were identified using inpatient records, or death registers until January 2021. Predicted metabolomic age was trained and tested based on 168 metabolomics. Metabolomic age was linked to the risk of 50 diseases in the validation dataset. The median follow-up duration for individual diseases ranged from 11.2 years to 11.9 years. After controlling for false discovery rate, chronological age-adjusted age gap (CAAG) was significantly associated with the incidence of 25 out of 50 chronic diseases. After adjustment for full covariates, associations with 15 chronic diseases remained significant. Greater CAAG was associated with increased risk of eight cardiometabolic disorders (including cardiovascular diseases and diabetes), some cancers, alcohol use disorder, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease and age-related macular degeneration. The association between CAAG and risk of peripheral vascular disease, other cardiac diseases, fracture, cataract and thyroid disorder was stronger among individuals with unhealthy diet than in those with healthy diet. The association between CAAG and risk of some conditions was stronger in younger individuals, those with metabolic disorders or low education. Metabolomic age plays an important role in the development of multiple chronic diseases. Healthy diet and high education may mitigate the risk for some chronic diseases due to metabolomic age acceleration.
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Affiliation(s)
- Xianwen Shang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- Guangdong Cardiovascular InstituteGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
- The Ophthalmic Epidemiology DepartmentCentre for Eye Research AustraliaMelbourneVictoriaAustralia
- Department of Medicine, Royal Melbourne HospitalUniversity of MelbourneMelbourneVictoriaAustralia
| | - Jiahao Liu
- The Ophthalmic Epidemiology DepartmentCentre for Eye Research AustraliaMelbourneVictoriaAustralia
| | - Zhuoting Zhu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- Guangdong Cardiovascular InstituteGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
- The Ophthalmic Epidemiology DepartmentCentre for Eye Research AustraliaMelbourneVictoriaAustralia
| | - Xueli Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- Medical Research Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Yu Huang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- Guangdong Cardiovascular InstituteGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
| | - Shunming Liu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic CenterSun Yat‐sen UniversityGuangzhouChina
| | - Xiayin Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- Guangdong Cardiovascular InstituteGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
| | - Shuo Ma
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Shulin Tang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Yijun Hu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Zongyuan Ge
- Monash e‐Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research CenterMonash UniversityMelbourneVictoriaAustralia
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Mingguang He
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- The Ophthalmic Epidemiology DepartmentCentre for Eye Research AustraliaMelbourneVictoriaAustralia
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic CenterSun Yat‐sen UniversityGuangzhouChina
- Experimental OphthalmologyThe Hong Kong Polytechnic UniversityHong KongChina
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Yuan Y, Huang L, Yu L, Yan X, Chen S, Bi C, He J, Zhao Y, Yang L, Ning L, Jin H, Yang R, Li Y. Clinical metabolomics characteristics of diabetic kidney disease: A meta-analysis of 1875 cases with diabetic kidney disease and 4503 controls. Diabetes Metab Res Rev 2024; 40:e3789. [PMID: 38501707 DOI: 10.1002/dmrr.3789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 01/01/2024] [Accepted: 01/31/2024] [Indexed: 03/20/2024]
Abstract
AIMS Diabetic Kidney Disease (DKD), one of the major complications of diabetes, is also a major cause of end-stage renal disease. Metabolomics can provide a unique metabolic profile of the disease and thus predict or diagnose the development of the disease. Therefore, this study summarises a more comprehensive set of clinical biomarkers related to DKD to identify functional metabolites significantly associated with the development of DKD and reveal their driving mechanisms for DKD. MATERIALS AND METHODS We searched PubMed, Embase, the Cochrane Library and Web of Science databases through October 2022. A meta-analysis was conducted on untargeted or targeted metabolomics research data based on the strategy of standardized mean differences and the process of ratio of means as the effect size, respectively. We compared the changes in metabolite levels between the DKD patients and the controls and explored the source of heterogeneity through subgroup analyses, sensitivity analysis and meta-regression analysis. RESULTS The 34 clinical-based metabolomics studies clarified the differential metabolites between DKD and controls, containing 4503 control subjects and 1875 patients with DKD. The results showed that a total of 60 common differential metabolites were found in both meta-analyses, of which 5 metabolites (p < 0.05) were identified as essential metabolites. Compared with the control group, metabolites glycine, aconitic acid, glycolic acid and uracil decreased significantly in DKD patients; cysteine was significantly higher. This indicates that amino acid metabolism, lipid metabolism and pyrimidine metabolism in DKD patients are disordered. CONCLUSIONS We have identified 5 metabolites and metabolic pathways related to DKD which can serve as biomarkers or targets for disease prevention and drug therapy.
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Affiliation(s)
- Yu Yuan
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Liping Huang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Lulu Yu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xingxu Yan
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Siyu Chen
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Chenghao Bi
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Junjie He
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yiqing Zhao
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Liu Yang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Li Ning
- Department Clinical Laboratory, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Hua Jin
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Rongrong Yang
- Public Health Science and Engineering College, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yubo Li
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
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Zhao J, Yu L, Sun K, Wang Y, Xie F. Nonlinear Relationship Between Systemic Immune-Inflammation and Hepatic Steatosis: A Population-Based Study in China. J Inflamm Res 2024; 17:711-720. [PMID: 38328561 PMCID: PMC10849142 DOI: 10.2147/jir.s440430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/23/2024] [Indexed: 02/09/2024] Open
Abstract
Background Studies on the associations between Systemic Immune-Inflammation (SII) and hepatic steatosis in China are still lacking. It is necessary to clarify the relationship between SII and hepatic steatosis in the Chinese population. Methods This study was conducted from January 2022 to December 2022. A total of 37,095 participants were enrolled, among them, with 20,709 (55.83%) being males, and 16,386 (44.17%) being females. Physical and biochemical indicators were measured during a morning health examination after the examinees had fasted overnight. Diagnoses of hepatic steatosis were determined using an ultrasound test in accordance with the Chinese Guideline. Analysis of variance and chi-square tests were used to analyze the association between SII and hepatic steatosis. Stratification analyses were conducted based on age, gender, and obese status. Restricted cubic spline regression was also performed to explore the shapes of associations between SII and hepatic steatosis. Results The average age of the 37,095 participants was 44.78 years old, with those with hepatic steatosis (11,599 (31.27%)) averaging 47.06 years old and those (25,496 (68.73%)) in the control group averaging 43.73 years old. SII was positively associated with hepatic steatosis. This association remained significant after conducting stratification analysis by age and gender. The inflection points in the inverted U-shaped curve for the relationship between SII and hepatic steatosis were 399.78 for gender (1000 cells /µL)(nonlinear P<0.01, OR=1.31 (male), 1.00 (female)) and 385.79 for age (1000 cells /µL)(nonlinear P<0.01, OR=1.35 (18~44 years old), 1.87 (45~59 years old), 1.93 (60~ years old)). Conclusion SII is an independent risk factor for hepatic steatosis, and this effect appears to be stronger in subjects with BMI <28 kg/m2. The nonlinear relationship between SII and hepatic steatosis, characterized by an inverted U-shaped distribution, may serve as a reference for diagnosing and evaluating hepatic steatosis.
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Affiliation(s)
- Jing Zhao
- Physical Examination Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, People’s Republic of China
| | - Li Yu
- Physical Examination Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, People’s Republic of China
| | - Kangyun Sun
- Physical Examination Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, People’s Republic of China
| | - Yun Wang
- Physical Examination Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, People’s Republic of China
| | - Fangfei Xie
- Physical Examination Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, People’s Republic of China
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Conde-Torres D, Blanco-González A, Seco-González A, Suárez-Lestón F, Cabezón A, Antelo-Riveiro P, Piñeiro Á, García-Fandiño R. Unraveling lipid and inflammation interplay in cancer, aging and infection for novel theranostic approaches. Front Immunol 2024; 15:1320779. [PMID: 38361953 PMCID: PMC10867256 DOI: 10.3389/fimmu.2024.1320779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 01/15/2024] [Indexed: 02/17/2024] Open
Abstract
The synergistic relationships between Cancer, Aging, and Infection, here referred to as the CAIn Triangle, are significant determinants in numerous health maladies and mortality rates. The CAIn-related pathologies exhibit close correlations with each other and share two common underlying factors: persistent inflammation and anomalous lipid concentration profiles in the membranes of affected cells. This study provides a comprehensive evaluation of the most pertinent interconnections within the CAIn Triangle, in addition to examining the relationship between chronic inflammation and specific lipidic compositions in cellular membranes. To tackle the CAIn-associated diseases, a suite of complementary strategies aimed at diagnosis, prevention, and treatment is proffered. Our holistic approach is expected to augment the understanding of the fundamental mechanisms underlying these diseases and highlight the potential of shared features to facilitate the development of novel theranostic strategies.
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Affiliation(s)
- Daniel Conde-Torres
- Departamento de Física Aplicada, Facultade de Física, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Alexandre Blanco-González
- Departamento de Física Aplicada, Facultade de Física, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- MD.USE Innovations S.L., Edificio Emprendia, Santiago de Compostela, Spain
| | - Alejandro Seco-González
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Fabián Suárez-Lestón
- Departamento de Física Aplicada, Facultade de Física, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- MD.USE Innovations S.L., Edificio Emprendia, Santiago de Compostela, Spain
| | - Alfonso Cabezón
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Paula Antelo-Riveiro
- Departamento de Física Aplicada, Facultade de Física, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Ángel Piñeiro
- Departamento de Física Aplicada, Facultade de Física, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Rebeca García-Fandiño
- Organic Chemistry Department, Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CiQUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
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Barosova R, Baranovicova E, Hanusrichterova J, Mokra D. Metabolomics in Animal Models of Bronchial Asthma and Its Translational Importance for Clinics. Int J Mol Sci 2023; 25:459. [PMID: 38203630 PMCID: PMC10779398 DOI: 10.3390/ijms25010459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/17/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
Bronchial asthma is an extremely heterogenous chronic respiratory disorder with several distinct endotypes and phenotypes. These subtypes differ not only in the pathophysiological changes and/or clinical features but also in their response to the treatment. Therefore, precise diagnostics represent a fundamental condition for effective therapy. In the diagnostic process, metabolomic approaches have been increasingly used, providing detailed information on the metabolic alterations associated with human asthma. Further information is brought by metabolomic analysis of samples obtained from animal models. This article summarizes the current knowledge on metabolomic changes in human and animal studies of asthma and reveals that alterations in lipid metabolism, amino acid metabolism, purine metabolism, glycolysis and the tricarboxylic acid cycle found in the animal studies resemble, to a large extent, the changes found in human patients with asthma. The findings indicate that, despite the limitations of animal modeling in asthma, pre-clinical testing and metabolomic analysis of animal samples may, together with metabolomic analysis of human samples, contribute to a novel way of personalized treatment of asthma patients.
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Affiliation(s)
- Romana Barosova
- Department of Physiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia; (R.B.); (J.H.)
| | - Eva Baranovicova
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia;
| | - Juliana Hanusrichterova
- Department of Physiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia; (R.B.); (J.H.)
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia;
| | - Daniela Mokra
- Department of Physiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia; (R.B.); (J.H.)
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Jové M, Mota-Martorell N, Fernàndez-Bernal A, Portero-Otin M, Barja G, Pamplona R. Phenotypic molecular features of long-lived animal species. Free Radic Biol Med 2023; 208:728-747. [PMID: 37748717 DOI: 10.1016/j.freeradbiomed.2023.09.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/12/2023] [Accepted: 09/21/2023] [Indexed: 09/27/2023]
Abstract
One of the challenges facing science/biology today is uncovering the molecular bases that support and determine animal and human longevity. Nature, in offering a diversity of animal species that differ in longevity by more than 5 orders of magnitude, is the best 'experimental laboratory' to achieve this aim. Mammals, in particular, can differ by more than 200-fold in longevity. For this reason, most of the available evidence on this topic derives from comparative physiology studies. But why can human beings, for instance, reach 120 years whereas rats only last at best 4 years? How does nature change the longevity of species? Longevity is a species-specific feature resulting from an evolutionary process. Long-lived animal species, including humans, show adaptations at all levels of biological organization, from metabolites to genome, supported by signaling and regulatory networks. The structural and functional features that define a long-lived species may suggest that longevity is a programmed biological property.
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Affiliation(s)
- Mariona Jové
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), University of Lleida (UdL), E25198, Lleida, Spain
| | - Natàlia Mota-Martorell
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), University of Lleida (UdL), E25198, Lleida, Spain
| | - Anna Fernàndez-Bernal
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), University of Lleida (UdL), E25198, Lleida, Spain
| | - Manuel Portero-Otin
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), University of Lleida (UdL), E25198, Lleida, Spain
| | - Gustavo Barja
- Department of Genetics, Physiology and Microbiology, Faculty of Biological Sciences, Complutense University of Madrid (UCM), E28040, Madrid, Spain
| | - Reinald Pamplona
- Department of Experimental Medicine, Lleida Biomedical Research Institute (IRBLleida), University of Lleida (UdL), E25198, Lleida, Spain.
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Liu D, Aziz NA, Landstra EN, Breteler MMB. The lipidomic correlates of epigenetic aging across the adult lifespan: A population-based study. Aging Cell 2023; 22:e13934. [PMID: 37496173 PMCID: PMC10497837 DOI: 10.1111/acel.13934] [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: 04/03/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/28/2023] Open
Abstract
Lipid signaling is involved in longevity regulation, but which specific lipid molecular species affect human biological aging remains largely unknown. We investigated the relation between complex lipids and DNA methylation-based metrics of biological aging among 4181 participants (mean age 55.1 years (range 30.0-95.0)) from the Rhineland Study, an ongoing population-based cohort study in Bonn, Germany. The absolute concentration of 14 lipid classes, covering 964 molecular species and 267 fatty acid composites, was measured by Metabolon Complex Lipid Panel. DNA methylation-based metrics of biological aging (AgeAccelPheno and AgeAccelGrim) were calculated based on published algorithms. Epigenome-wide association analyses (EWAS) of biological aging-associated lipids and pathway analysis were performed to gain biological insights into the mechanisms underlying the effects of lipidomics on biological aging. We found that higher levels of molecular species belonging to neutral lipids, phosphatidylethanolamines, phosphatidylinositols, and dihydroceramides were associated with faster biological aging, whereas higher levels of lysophosphatidylcholine, hexosylceramide, and lactosylceramide species were associated with slower biological aging. Ceramide, phosphatidylcholine, and lysophosphatidylethanolamine species with odd-numbered fatty acid tail lengths were associated with slower biological aging, whereas those with even-numbered chain lengths were associated with faster biological aging. EWAS combined with functional pathway analysis revealed several complex lipids associated with biological aging as important regulators of known longevity and aging-related pathways.
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Affiliation(s)
- Dan Liu
- Population Health SciencesGerman Center for Neurodegenerative Diseases (DZNE)BonnGermany
| | - N. Ahmad Aziz
- Population Health SciencesGerman Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Department of Neurology, Faculty of MedicineUniversity of BonnBonnGermany
| | - Elvire Nadieh Landstra
- Population Health SciencesGerman Center for Neurodegenerative Diseases (DZNE)BonnGermany
| | - Monique M. B. Breteler
- Population Health SciencesGerman Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of MedicineUniversity of BonnBonnGermany
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9
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de Jonckheere B, Kollotzek F, Münzer P, Göb V, Fischer M, Mott K, Coman C, Troppmair NN, Manke MC, Zdanyte M, Harm T, Sigle M, Kopczynski D, Bileck A, Gerner C, Hoffmann N, Heinzmann D, Assinger A, Gawaz M, Stegner D, Schulze H, Borst O, Ahrends R. Critical shifts in lipid metabolism promote megakaryocyte differentiation and proplatelet formation. NATURE CARDIOVASCULAR RESEARCH 2023; 2:835-852. [PMID: 38075556 PMCID: PMC7615361 DOI: 10.1038/s44161-023-00325-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 07/27/2023] [Indexed: 08/29/2024]
Abstract
During megakaryopoiesis, megakaryocytes (MK) undergo cellular morphological changes with strong modification of membrane composition and lipid signaling. Here we adopt a lipid-centric multiomics approach to create a quantitative map of the MK lipidome during maturation and proplatelet formation. Data reveal that MK differentiation is driven by an increased fatty acyl import and de novo lipid synthesis, resulting in an anionic membrane phenotype. Pharmacological perturbation of fatty acid import and phospholipid synthesis blocked membrane remodeling and directly reduced MK polyploidization and proplatelet formation resulting in thrombocytopenia. The anionic lipid shift during megakaryopoiesis was paralleled by lipid-dependent relocalization of the scaffold protein CKIP-1 and recruitment of the kinase CK2α to the plasma membrane, which seems to be essential for sufficient platelet biogenesis. Overall, this study provides a framework to understand how the MK lipidome is altered during maturation and the impact of MK membrane lipid remodeling on MK kinase signaling involved in thrombopoiesis.
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Affiliation(s)
- Bianca de Jonckheere
- Institute of Analytical Chemistry, University of Vienna, Austria
- Vienna Doctoral School in Chemistry, University of Vienna, Austria
| | - Ferdinand Kollotzek
- DFG Heisenberg Group Cardiovascular Thromboinflammation and Translational Thrombocardiology, University of Tübingen, Germany
- Department of Cardiology and Angiology, University of Tübingen, Germany
| | - Patrick Münzer
- DFG Heisenberg Group Cardiovascular Thromboinflammation and Translational Thrombocardiology, University of Tübingen, Germany
- Department of Cardiology and Angiology, University of Tübingen, Germany
| | - Vanessa Göb
- Institute for Experimental Biomedicine, University Hospital Würzburg, Germany
- Rudolf Virchow Center for Integrative and Translational Bioimaging, Würzburg, Germany
| | - Melina Fischer
- DFG Heisenberg Group Cardiovascular Thromboinflammation and Translational Thrombocardiology, University of Tübingen, Germany
- Department of Cardiology and Angiology, University of Tübingen, Germany
| | - Kristina Mott
- Institute for Experimental Biomedicine, University Hospital Würzburg, Germany
| | - Cristina Coman
- Institute of Analytical Chemistry, University of Vienna, Austria
| | - Nina Nicole Troppmair
- Institute of Analytical Chemistry, University of Vienna, Austria
- Vienna Doctoral School in Chemistry, University of Vienna, Austria
| | - Mailin-Christin Manke
- DFG Heisenberg Group Cardiovascular Thromboinflammation and Translational Thrombocardiology, University of Tübingen, Germany
- Department of Cardiology and Angiology, University of Tübingen, Germany
| | - Monika Zdanyte
- DFG Heisenberg Group Cardiovascular Thromboinflammation and Translational Thrombocardiology, University of Tübingen, Germany
- Department of Cardiology and Angiology, University of Tübingen, Germany
| | - Tobias Harm
- Department of Cardiology and Angiology, University of Tübingen, Germany
| | - Manuel Sigle
- Department of Cardiology and Angiology, University of Tübingen, Germany
| | | | - Andrea Bileck
- Institute of Analytical Chemistry, University of Vienna, Austria
- Joint Metabolome Facility, University of Vienna and Medical University of Vienna, Austria
| | - Christopher Gerner
- Institute of Analytical Chemistry, University of Vienna, Austria
- Joint Metabolome Facility, University of Vienna and Medical University of Vienna, Austria
| | - Nils Hoffmann
- Institute of Analytical Chemistry, University of Vienna, Austria
- Forschungszentrum Jülich GmbH, Institute for Bio- and Geosciences (IBG-5) Jülich, Germany
| | - David Heinzmann
- Department of Cardiology and Angiology, University of Tübingen, Germany
| | - Alice Assinger
- Institute of Physiology, Centre of Physiology and Pharmacology, Medical University of Vienna, Austria
| | - Meinrad Gawaz
- Department of Cardiology and Angiology, University of Tübingen, Germany
| | - David Stegner
- Institute for Experimental Biomedicine, University Hospital Würzburg, Germany
- Rudolf Virchow Center for Integrative and Translational Bioimaging, Würzburg, Germany
| | - Harald Schulze
- Institute for Experimental Biomedicine, University Hospital Würzburg, Germany
| | - Oliver Borst
- DFG Heisenberg Group Cardiovascular Thromboinflammation and Translational Thrombocardiology, University of Tübingen, Germany
- Department of Cardiology and Angiology, University of Tübingen, Germany
| | - Robert Ahrends
- Institute of Analytical Chemistry, University of Vienna, Austria
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10
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Cedillo L, Ahsan FM, Li S, Stuhr NL, Zhou Y, Zhang Y, Adedoja A, Murphy LM, Yerevanian A, Emans S, Dao K, Li Z, Peterson ND, Watrous J, Jain M, Das S, Pukkila-Worley R, Curran SP, Soukas AA. Ether lipid biosynthesis promotes lifespan extension and enables diverse pro-longevity paradigms in Caenorhabditis elegans. eLife 2023; 12:e82210. [PMID: 37606250 PMCID: PMC10444025 DOI: 10.7554/elife.82210] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 07/13/2023] [Indexed: 08/23/2023] Open
Abstract
Biguanides, including the world's most prescribed drug for type 2 diabetes, metformin, not only lower blood sugar, but also promote longevity in preclinical models. Epidemiologic studies in humans parallel these findings, indicating favorable effects of metformin on longevity and on reducing the incidence and morbidity associated with aging-related diseases. Despite this promise, the full spectrum of molecular effectors responsible for these health benefits remains elusive. Through unbiased screening in Caenorhabditis elegans, we uncovered a role for genes necessary for ether lipid biosynthesis in the favorable effects of biguanides. We demonstrate that biguanides prompt lifespan extension by stimulating ether lipid biogenesis. Loss of the ether lipid biosynthetic machinery also mitigates lifespan extension attributable to dietary restriction, target of rapamycin (TOR) inhibition, and mitochondrial electron transport chain inhibition. A possible mechanistic explanation for this finding is that ether lipids are required for activation of longevity-promoting, metabolic stress defenses downstream of the conserved transcription factor skn-1/Nrf. In alignment with these findings, overexpression of a single, key, ether lipid biosynthetic enzyme, fard-1/FAR1, is sufficient to promote lifespan extension. These findings illuminate the ether lipid biosynthetic machinery as a novel therapeutic target to promote healthy aging.
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Affiliation(s)
- Lucydalila Cedillo
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Broad Institute of Harvard and MITCambridgeUnited States
- Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical SchoolBostonUnited States
| | - Fasih M Ahsan
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Broad Institute of Harvard and MITCambridgeUnited States
- Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical SchoolBostonUnited States
| | - Sainan Li
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Broad Institute of Harvard and MITCambridgeUnited States
| | - Nicole L Stuhr
- Leonard Davis School of Gerontology, University of Southern CaliforniaLos AngelesUnited States
| | - Yifei Zhou
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Broad Institute of Harvard and MITCambridgeUnited States
| | - Yuyao Zhang
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Broad Institute of Harvard and MITCambridgeUnited States
| | - Adebanjo Adedoja
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Broad Institute of Harvard and MITCambridgeUnited States
- Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical SchoolBostonUnited States
| | - Luke M Murphy
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Broad Institute of Harvard and MITCambridgeUnited States
- Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical SchoolBostonUnited States
| | - Armen Yerevanian
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Broad Institute of Harvard and MITCambridgeUnited States
| | - Sinclair Emans
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Broad Institute of Harvard and MITCambridgeUnited States
| | - Khoi Dao
- Department of Medicine and Pharmacology, University of California San DiegoSan DiegoUnited States
| | - Zhaozhi Li
- Biomedical Informatics Core, Massachusetts General Hospital and Harvard Medical SchooCambridgeUnited States
| | - Nicholas D Peterson
- Program in Innate Immunity, Division of Infectious Diseases and Immunology, University of Massachusetts Medical SchoolWorcesterUnited States
| | - Jeramie Watrous
- Department of Medicine and Pharmacology, University of California San DiegoSan DiegoUnited States
| | - Mohit Jain
- Department of Medicine and Pharmacology, University of California San DiegoSan DiegoUnited States
| | - Sudeshna Das
- Biomedical Informatics Core, Massachusetts General Hospital and Harvard Medical SchooCambridgeUnited States
| | - Read Pukkila-Worley
- Program in Innate Immunity, Division of Infectious Diseases and Immunology, University of Massachusetts Medical SchoolWorcesterUnited States
| | - Sean P Curran
- Leonard Davis School of Gerontology, University of Southern CaliforniaLos AngelesUnited States
| | - Alexander A Soukas
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Broad Institute of Harvard and MITCambridgeUnited States
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11
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Lista S, González-Domínguez R, López-Ortiz S, González-Domínguez Á, Menéndez H, Martín-Hernández J, Lucia A, Emanuele E, Centonze D, Imbimbo BP, Triaca V, Lionetto L, Simmaco M, Cuperlovic-Culf M, Mill J, Li L, Mapstone M, Santos-Lozano A, Nisticò R. Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives. Ageing Res Rev 2023; 89:101987. [PMID: 37343679 DOI: 10.1016/j.arr.2023.101987] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 06/23/2023]
Abstract
Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.
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Affiliation(s)
- Simone Lista
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), Valladolid, Spain.
| | - Raúl González-Domínguez
- Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, Cádiz, Spain
| | - Susana López-Ortiz
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), Valladolid, Spain
| | - Álvaro González-Domínguez
- Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, Cádiz, Spain
| | - Héctor Menéndez
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), Valladolid, Spain
| | - Juan Martín-Hernández
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), Valladolid, Spain
| | - Alejandro Lucia
- Research Institute of the Hospital 12 de Octubre ('imas12'), Madrid, Spain; Faculty of Sport Sciences, European University of Madrid, Villaviciosa de Odón, Madrid, Spain; CIBER of Frailty and Healthy Ageing (CIBERFES), Madrid, Spain
| | | | - Diego Centonze
- Department of Systems Medicine, Tor Vergata University, Rome, Italy; Unit of Neurology, IRCCS Neuromed, Pozzilli, IS, Italy
| | - Bruno P Imbimbo
- Department of Research and Development, Chiesi Farmaceutici, Parma, Italy
| | - Viviana Triaca
- Institute of Biochemistry and Cell Biology (IBBC), National Research Council (CNR), Rome, Italy
| | - Luana Lionetto
- Clinical Biochemistry, Mass Spectrometry Section, Sant'Andrea University Hospital, Rome, Italy; Department of Neuroscience, Mental Health and Sensory Organs, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Maurizio Simmaco
- Clinical Biochemistry, Mass Spectrometry Section, Sant'Andrea University Hospital, Rome, Italy; Department of Neuroscience, Mental Health and Sensory Organs, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Miroslava Cuperlovic-Culf
- Digital Technologies Research Center, National Research Council, Ottawa, Canada; Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, ON, Canada
| | - Jericha Mill
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA; School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA
| | - Mark Mapstone
- Department of Neurology, Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, Irvine, CA, USA
| | - Alejandro Santos-Lozano
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), Valladolid, Spain; Research Institute of the Hospital 12 de Octubre ('imas12'), Madrid, Spain
| | - Robert Nisticò
- School of Pharmacy, University of Rome "Tor Vergata", Rome, Italy; Laboratory of Pharmacology of Synaptic Plasticity, EBRI Rita Levi-Montalcini Foundation, Rome, Italy
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12
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Lee S, Kim S, Kim SD, Oh SJ, Kong SK, Lee HM, Kim S, Choi SW. Differences in the metabolomic profile of the human palatine tonsil between pediatrics and adults. PLoS One 2023; 18:e0288871. [PMID: 37523386 PMCID: PMC10389742 DOI: 10.1371/journal.pone.0288871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 07/05/2023] [Indexed: 08/02/2023] Open
Abstract
Palatine tonsils (PT) are B cell-predominant lymphoid organs that provide primary immune responses to airborne and dietary pathogens. Numerous histopathological and immunological studies have been conducted on PT, yet no investigations have been conducted on its metabolic profile. We performed high-resolution magic angle spinning nuclear magnetic resonance spectroscopy-based metabolic profiling in 35 pediatric and 28 adult human palatine tonsillar tissue samples. A total of 36 metabolites were identified, and the levels of 10 metabolites were significantly different depending on age. Among them, partial correlation analysis shows that glucose levels increased with age, whereas glycine, phosphocholine, phosphoethanolamine, and ascorbate levels decreased with age. We confirmed the decrease in immunometabolic activity in adults through metabolomic analysis, which had been anticipated from previous histological and immunological studies on the PT. These results improve our understanding of metabolic changes in the PT with aging and serve as a basis for future tonsil-related metabolomic studies.
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Affiliation(s)
- Seokhwan Lee
- Department of Otorhinolaryngology, Inje University Haeundae Paik Hospital, Busan, Republic of Korea
| | - Seonghye Kim
- Department of Chemistry and Chemistry Institute for Functional Materials, Pusan National University, Busan, Republic of Korea
| | - Sung-Dong Kim
- Department of Otorhinolaryngology and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Se-Joon Oh
- Department of Otorhinolaryngology and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Soo-Keun Kong
- Department of Otorhinolaryngology and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Hyun-Min Lee
- Department of Otorhinolaryngology and Biomedical Research Institute, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | - Suhkmann Kim
- Department of Chemistry and Chemistry Institute for Functional Materials, Pusan National University, Busan, Republic of Korea
| | - Sung-Won Choi
- Department of Otorhinolaryngology and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
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13
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Wang X, Jung HJ, Milholland B, Cui J, Tazearslan C, Atzmon G, Wang X, Yang J, Guo Q, Barzilai N, Robbins PD, Suh Y. The regulation of Insulin/IGF-1 signaling by miR-142-3p associated with human longevity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.19.541542. [PMID: 37292828 PMCID: PMC10245758 DOI: 10.1101/2023.05.19.541542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
MicroRNAs (miRNAs) have been demonstrated to modulate life span in the invertebrates C. elegans and Drosophila by targeting conserved pathways of aging, such as insulin/IGF-1 signaling (IIS). However, a role for miRNAs in modulating human longevity has not been fully explored. Here we investigated novel roles of miRNAs as a major epigenetic component of exceptional longevity in humans. By profiling the miRNAs in B-cells from Ashkenazi Jewish centenarians and 70-year-old controls without a longevity history, we found that the majority of differentially expressed miRNAs were upregulated in centenarians and predicted to modulate the IIS pathway. Notably, decreased IIS activity was found in B cells from centenarians who harbored these upregulated miRNAs. miR-142-3p, the top upregulated miRNA, was verified to dampen the IIS pathway by targeting multiple genes including GNB2, AKT1S1, RHEB and FURIN . Overexpression of miR-142-3p improved the stress resistance under genotoxicity and induced the impairment of cell cycle progression in IMR90 cells. Furthermore, mice injected with a miR-142-3p mimic showed reduced IIS signaling and improved longevity-associated phenotypes including enhanced stress resistance, improved diet/aging-induced glucose intolerance, and longevity-associated change of metabolic profile. These data suggest that miR-142-3p is involved in human longevity through regulating IIS-mediated pro-longevity effects. This study provides strong support for the use of miR-142-3p as a novel therapeutic to promote longevity or prevent aging/aging-related diseases in human.
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14
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Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Citation(s) in RCA: 99] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
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Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
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15
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St Sauver JL, LeBrasseur NK, Rocca WA, Olson JE, Bielinski SJ, Sohn S, Weston SA, McGree ME, Mielke MM. Cohort study examining associations between ceramide levels and risk of multimorbidity among persons participating in the Mayo Clinic Biobank. BMJ Open 2023; 13:e069375. [PMID: 37085302 PMCID: PMC10124265 DOI: 10.1136/bmjopen-2022-069375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/23/2023] Open
Abstract
OBJECTIVE Ceramides have been associated with several ageing-related conditions but have not been studied as a general biomarker of multimorbidity (MM). Therefore, we determined whether ceramide levels are associated with the rapid development of MM. DESIGN Retrospective cohort study. SETTING Mayo Clinic Biobank. PARTICIPANTS 1809 persons in the Mayo Clinic Biobank ≥65 years without MM at the time of enrolment, and with ceramide levels assayed from stored plasma. PRIMARY OUTCOME MEASURE Persons were followed for a median of 5.7 years through their medical records to identify new diagnoses of 20 chronic conditions. The number of new conditions was divided by the person-years of follow-up to calculate the rate of accumulation of new chronic conditions. RESULTS Higher levels of C18:0 and C20:0 were associated with a more rapid rate of accumulation of chronic conditions (C18:0 z score RR: 1.30, 95% CI: 1.10 to 1.53; C20:0 z score RR: 1.26, 95% CI: 1.07 to 1.49). Higher C18:0 and C20:0 levels were also associated with an increased risk of hypertension and coronary artery disease. CONCLUSIONS C18:0 and C20:0 were associated with an increased risk of cardiometabolic conditions. When combined with biomarkers specific to other diseases of ageing, these ceramides may be a useful component of a biomarker panel for predicting accelerated ageing.
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Affiliation(s)
- Jennifer L St Sauver
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Nathan K LeBrasseur
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, Minnesota, USA
| | - Walter A Rocca
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Janet E Olson
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Suzette J Bielinski
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Sunghwan Sohn
- Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Susan A Weston
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Michaela E McGree
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Michelle M Mielke
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
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16
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Medina J, Borreggine R, Teav T, Gao L, Ji S, Carrard J, Jones C, Blomberg N, Jech M, Atkins A, Martins C, Schmidt-Trucksass A, Giera M, Cazenave-Gassiot A, Gallart-Ayala H, Ivanisevic J. Omic-Scale High-Throughput Quantitative LC-MS/MS Approach for Circulatory Lipid Phenotyping in Clinical Research. Anal Chem 2023; 95:3168-3179. [PMID: 36716250 DOI: 10.1021/acs.analchem.2c02598] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Lipid analysis at the molecular species level represents a valuable opportunity for clinical applications due to the essential roles that lipids play in metabolic health. However, a comprehensive and high-throughput lipid profiling remains challenging given the lipid structural complexity and exceptional diversity. Herein, we present an 'omic-scale targeted LC-MS/MS approach for the straightforward and high-throughput quantification of a broad panel of complex lipid species across 26 lipid (sub)classes. The workflow involves an automated single-step extraction with 2-propanol, followed by lipid analysis using hydrophilic interaction liquid chromatography in a dual-column setup coupled to tandem mass spectrometry with data acquisition in the timed-selective reaction monitoring mode (12 min total run time). The analysis pipeline consists of an initial screen of 1903 lipid species, followed by high-throughput quantification of robustly detected species. Lipid quantification is achieved by a single-point calibration with 75 isotopically labeled standards representative of different lipid classes, covering lipid species with diverse acyl/alkyl chain lengths and unsaturation degrees. When applied to human plasma, 795 lipid species were measured with median intra- and inter-day precisions of 8.5 and 10.9%, respectively, evaluated within a single and across multiple batches. The concentration ranges measured in NIST plasma were in accordance with the consensus intervals determined in previous ring-trials. Finally, to benchmark our workflow, we characterized NIST plasma materials with different clinical and ethnic backgrounds and analyzed a sub-set of sera (n = 81) from a clinically healthy elderly population. Our quantitative lipidomic platform allowed for a clear distinction between different NIST materials and revealed the sex-specificity of the serum lipidome, highlighting numerous statistically significant sex differences.
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Affiliation(s)
- Jessica Medina
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Rebecca Borreggine
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Tony Teav
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Liang Gao
- Department of Biochemistry and Precision Medicine TRP, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore.,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore 117456, Singapore
| | - Shanshan Ji
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore 117456, Singapore
| | - Justin Carrard
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, Basel CH-4052, Switzerland
| | - Christina Jones
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Niek Blomberg
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden 2333ZA, Netherlands
| | - Martin Jech
- Thermo Fisher Scientific, 355 River Oaks Pkwy, San Jose, California 95134, United States
| | - Alan Atkins
- Thermo Fisher Scientific, 355 River Oaks Pkwy, San Jose, California 95134, United States
| | - Claudia Martins
- Thermo Fisher Scientific, 355 River Oaks Pkwy, San Jose, California 95134, United States
| | - Arno Schmidt-Trucksass
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, Basel CH-4052, Switzerland
| | - Martin Giera
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden 2333ZA, Netherlands
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry and Precision Medicine TRP, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore.,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore 117456, Singapore
| | - Hector Gallart-Ayala
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
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17
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Staab TA, McIntyre G, Wang L, Radeny J, Bettcher L, Guillen M, Peck MP, Kalil AP, Bromley SP, Raftery D, Chan JP. The lipidomes of C. elegans with mutations in asm-3/acid sphingomyelinase and hyl-2/ceramide synthase show distinct lipid profiles during aging. Aging (Albany NY) 2023; 15:650-674. [PMID: 36787434 PMCID: PMC9970312 DOI: 10.18632/aging.204515] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 02/01/2023] [Indexed: 02/16/2023]
Abstract
Lipid metabolism affects cell and physiological functions that mediate animal healthspan and lifespan. Lipidomics approaches in model organisms have allowed us to better understand changes in lipid composition related to age and lifespan. Here, using the model C. elegans, we examine the lipidomes of mutants lacking enzymes critical for sphingolipid metabolism; specifically, we examine acid sphingomyelinase (asm-3), which breaks down sphingomyelin to ceramide, and ceramide synthase (hyl-2), which synthesizes ceramide from sphingosine. Worm asm-3 and hyl-2 mutants have been previously found to be long- and short-lived, respectively. We analyzed longitudinal lipid changes in wild type animals compared to mutants at 1-, 5-, and 10-days of age. We detected over 700 different lipids in several lipid classes. Results indicate that wildtype animals exhibit increased triacylglycerols (TAG) at 10-days compared to 1-day, and decreased lysophoshatidylcholines (LPC). We find that 10-day hyl-2 mutants have elevated total polyunsaturated fatty acids (PUFA) and increased LPCs compared to 10-day wildtype animals. These changes mirror another short-lived model, the daf-16/FOXO transcription factor that is downstream of the insulin-like signaling pathway. In addition, we find that hyl-2 mutants have poor oxidative stress response, supporting a model where mutants with elevated PUFAs may accumulate more oxidative damage. On the other hand, 10-day asm-3 mutants have fewer TAGs. Intriguingly, asm-3 mutants have a similar lipid composition as the long-lived, caloric restriction model eat-2/mAChR mutant. Together, these analyses highlight the utility of lipidomic analyses to characterize metabolic changes during aging in C. elegans.
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Affiliation(s)
- Trisha A. Staab
- Department of Biology, Marian University, Indianapolis, IN 46222, USA
| | - Grace McIntyre
- Department of Biology, Marian University, Indianapolis, IN 46222, USA
| | - Lu Wang
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA
| | - Joycelyn Radeny
- Department of Biology, Juniata College, Huntingdon, PA 16652, USA
| | - Lisa Bettcher
- Northwest Metabolomics Research Center, University of Washington, Seattle, WA 98195, USA
| | - Melissa Guillen
- Department of Biology, Marian University, Indianapolis, IN 46222, USA
| | - Margaret P. Peck
- Department of Biology, Juniata College, Huntingdon, PA 16652, USA
| | - Azia P. Kalil
- Department of Biology, Juniata College, Huntingdon, PA 16652, USA
| | | | - Daniel Raftery
- Northwest Metabolomics Research Center, University of Washington, Seattle, WA 98195, USA
| | - Jason P. Chan
- Department of Biology, Marian University, Indianapolis, IN 46222, USA
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18
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He M, Slee EA, Sun M, Hu C, Chang WT, Xu G, Lu X, Wang M. Defect in Ser312 phosphorylation of Tp53 dysregulates lipid metabolism for fatty accumulation and fatty liver susceptibility: Revealed by lipidomics. J Chromatogr B Analyt Technol Biomed Life Sci 2022; 1211:123491. [DOI: 10.1016/j.jchromb.2022.123491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 08/20/2022] [Accepted: 09/29/2022] [Indexed: 11/29/2022]
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19
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Lu C, Liu C, Mei D, Yu M, Bai J, Bao X, Wang M, Fu K, Yi X, Ge W, Shen J, Peng Y, Xu W. Comprehensive metabolomic characterization of atrial fibrillation. Front Cardiovasc Med 2022; 9:911845. [PMID: 36003904 PMCID: PMC9393302 DOI: 10.3389/fcvm.2022.911845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 07/12/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundUsing human humoral metabolomic profiling, we can discover the diagnostic biomarkers and pathogenesis of disease. The specific characterization of atrial fibrillation (AF) subtypes with metabolomics may facilitate effective and targeted treatment, especially in early stages.ObjectivesBy investigating disturbed metabolic pathways, we could evaluate the diagnostic value of biomarkers based on metabolomics for different types of AF.MethodsA cohort of 363 patients was enrolled and divided into a discovery and validation set. Patients underwent an electrocardiogram (ECG) for suspected AF. Groups were divided as follows: healthy individuals (Control), suspected AF (Sus-AF), first diagnosed AF (Fir-AF), paroxysmal AF (Par-AF), persistent AF (Per-AF), and AF causing a cardiogenic ischemic stroke (Car-AF). Serum metabolomic profiles were determined by gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Metabolomic variables were analyzed with clinical information to identify relevant diagnostic biomarkers.ResultsThe metabolic disorders were characterized by 16 cross-comparisons. We focused on comparing all of the types of AF (All-AFs) plus Car-AF vs. Control, All-AFs vs. Car-AF, Par-AF vs. Control, and Par-AF vs. Per-AF. Then, 117 and 94 metabolites were identified by GC/MS and LC-QTOF-MS, respectively. The essential altered metabolic pathways during AF progression included D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, etc. For differential diagnosis, the area under the curve (AUC) of specific metabolomic biomarkers ranged from 0.8237 to 0.9890 during the discovery phase, and the predictive values in the validation cohort were 78.8–90.2%.ConclusionsSerum metabolomics is a powerful way to identify metabolic disturbances. Differences in small–molecule metabolites may serve as biomarkers for AF onset, progression, and differential diagnosis.
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20
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Zhang W, Xiong Y, Tao R, Panayi AC, Mi B, Liu G. Emerging Insight Into the Role of Circadian Clock Gene BMAL1 in Cellular Senescence. Front Endocrinol (Lausanne) 2022; 13:915139. [PMID: 35733785 PMCID: PMC9207346 DOI: 10.3389/fendo.2022.915139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/10/2022] [Indexed: 12/16/2022] Open
Abstract
Cell senescence is a crucial process in cell fate determination and is involved in an extensive array of aging-associated diseases. General perceptions and experimental evidence point out that the decline of physical function as well as aging-associated diseases are often initiated by cell senescence and organ ageing. Therefore, regulation of cell senescence process can be a promising way to handle aging-associated diseases such as osteoporosis. The circadian clock regulates a wide range of cellular and physiological activities, and many age-linked degenerative disorders are associated with the dysregulation of clock genes. BMAL1 is a core circadian transcription factor and governs downstream genes by binding to the E-box elements in their promoters. Compelling evidence has proposed the role of BMAL1 in cellular senescence and aging-associated diseases. In this review, we summarize the linkage between BMAL1 and factors of cell senescence including oxidative stress, metabolism, and the genotoxic stress response. Dysregulated and dampened BMAL1 may serve as a potential therapeutic target against aging- associated diseases.
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Affiliation(s)
- Wenqian Zhang
- Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - Yuan Xiong
- Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - Ranyang Tao
- Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - Adriana C. Panayi
- Division of Plastic Surgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Bobin Mi
- Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - Guohui Liu
- Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
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21
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Unfried M, Ng LF, Cazenave-Gassiot A, Batchu KC, Kennedy BK, Wenk MR, Tolwinski N, Gruber J. LipidClock: A Lipid-Based Predictor of Biological Age. FRONTIERS IN AGING 2022; 3:828239. [PMID: 35821819 PMCID: PMC9261347 DOI: 10.3389/fragi.2022.828239] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 04/01/2022] [Indexed: 11/29/2022]
Abstract
Complexity is a fundamental feature of biological systems. Omics techniques like lipidomics can simultaneously quantify many thousands of molecules, thereby directly capturing the underlying biological complexity. However, this approach transfers the original biological complexity to the resulting datasets, posing challenges in data reduction and analysis. Aging is a prime example of a process that exhibits complex behaviour across multiple scales of biological organisation. The aging process is characterised by slow, cumulative and detrimental changes that are driven by intrinsic biological stochasticity and mediated through non-linear interactions and feedback within and between these levels of organization (ranging from metabolites, macromolecules, organelles and cells to tissue and organs). Only collectively and over long timeframes do these changes manifest as the exponential increases in morbidity and mortality that define biological aging, making aging a problem more difficult to study than the aetiologies of specific diseases. But aging's time dependence can also be exploited to extract key insights into its underlying biology. Here we explore this idea by using data on changes in lipid composition across the lifespan of an organism to construct and test a LipidClock to predict biological age in the nematode Caenorhabdits elegans. The LipidClock consist of a feature transformation via Principal Component Analysis followed by Elastic Net regression and yields and Mean Absolute Error of 1.45 days for wild type animals and 4.13 days when applied to mutant strains with lifespans that are substantially different from that of wild type. Gompertz aging rates predicted by the LipidClock can be used to simulate survival curves that are in agreement with those from lifespan experiments.
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Affiliation(s)
- Maximilian Unfried
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Li Fang Ng
- Science Divisions, Yale-NUS College, Singapore, Singapore
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | | | - Brian K. Kennedy
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Markus R. Wenk
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Nicholas Tolwinski
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Science Divisions, Yale-NUS College, Singapore, Singapore
| | - Jan Gruber
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Science Divisions, Yale-NUS College, Singapore, Singapore
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22
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Magalhães S, Almeida I, Pereira CD, Rebelo S, Goodfellow BJ, Nunes A. The Long-Term Culture of Human Fibroblasts Reveals a Spectroscopic Signature of Senescence. Int J Mol Sci 2022; 23:ijms23105830. [PMID: 35628639 PMCID: PMC9146002 DOI: 10.3390/ijms23105830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/16/2022] [Accepted: 05/17/2022] [Indexed: 11/24/2022] Open
Abstract
Aging is a complex process which leads to progressive loss of fitness/capability/ability, increasing susceptibility to disease and, ultimately, death. Regardless of the organism, there are some features common to aging, namely, the loss of proteostasis and cell senescence. Mammalian cell lines have been used as models to study the aging process, in particular, cell senescence. Thus, the aim of this study was to characterize the senescence-associated metabolic profile of a long-term culture of human fibroblasts using Fourier Transform Infrared and Nuclear Magnetic Resonance spectroscopy. We sub-cultivated fibroblasts from a newborn donor from passage 4 to passage 17 and the results showed deep changes in the spectroscopic profile of cells over time. Late passage cells were characterized by a decrease in the length of fatty acid chains, triglycerides and cholesterol and an increase in lipid unsaturation. We also found an increase in the content of intermolecular β-sheets, possibly indicating an increase in protein aggregation levels in cells of later passages. Metabolic profiling by NMR showed increased levels of extracellular lactate, phosphocholine and glycine in cells at later passages. This study suggests that spectroscopy approaches can be successfully used to study changes concomitant with cell senescence and validate the use of human fibroblasts as a model to monitor the aging process.
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Affiliation(s)
- Sandra Magalhães
- iBiMED—Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Agra do Crasto, 3810-193 Aveiro, Portugal; (S.M.); (I.A.); (C.D.P.); (S.R.)
- CICECO—Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal;
| | - Idália Almeida
- iBiMED—Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Agra do Crasto, 3810-193 Aveiro, Portugal; (S.M.); (I.A.); (C.D.P.); (S.R.)
- CICECO—Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal;
| | - Cátia D. Pereira
- iBiMED—Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Agra do Crasto, 3810-193 Aveiro, Portugal; (S.M.); (I.A.); (C.D.P.); (S.R.)
| | - Sandra Rebelo
- iBiMED—Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Agra do Crasto, 3810-193 Aveiro, Portugal; (S.M.); (I.A.); (C.D.P.); (S.R.)
| | - Brian J. Goodfellow
- CICECO—Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal;
| | - Alexandra Nunes
- iBiMED—Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Agra do Crasto, 3810-193 Aveiro, Portugal; (S.M.); (I.A.); (C.D.P.); (S.R.)
- Correspondence: ; Tel.: +351-234-324-435
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23
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Xu Q, Wu C, Zhu Q, Gao R, Lu J, Valles-Colomer M, Zhu J, Yin F, Huang L, Ding L, Zhang X, Zhang Y, Xiong X, Bi M, Chen X, Zhu Y, Liu L, Liu Y, Chen Y, Fan J, Sun Y, Wang J, Cao Z, Fan C, Ehrlich SD, Segata N, Qin N, Qin H. Metagenomic and metabolomic remodeling in nonagenarians and centenarians and its association with genetic and socioeconomic factors. NATURE AGING 2022; 2:438-452. [PMID: 37118062 DOI: 10.1038/s43587-022-00193-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 02/16/2022] [Indexed: 04/30/2023]
Abstract
A better understanding of the biological and environmental variables that contribute to exceptional longevity has the potential to inform the treatment of geriatric diseases and help achieve healthy aging. Here, we compared the gut microbiome and blood metabolome of extremely long-lived individuals (94-105 years old) to that of their children (50-79 years old) in 116 Han Chinese families. We found extensive metagenomic and metabolomic remodeling in advanced age and observed a generational divergence in the correlations with socioeconomic factors. An analysis of quantitative trait loci revealed that genetic associations with metagenomic and metabolomic features were largely generation-specific, but we also found 131 plasma metabolic quantitative trait loci associations that were cross-generational with the genetic variants concentrated in six loci. These included associations between FADS1/2 and arachidonate, PTPA and succinylcarnitine and FLVCR1 and choline. Our characterization of the extensive metagenomic and metabolomic remodeling that occurs in people reaching extreme ages may offer new targets for aging-related interventions.
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Affiliation(s)
- Qian Xu
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chunyan Wu
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Qi Zhu
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Renyuan Gao
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jianquan Lu
- Qidong People's Hospital/Qidong Liver Cancer Institute, Qidong, China
| | | | - Jian Zhu
- Qidong People's Hospital/Qidong Liver Cancer Institute, Qidong, China
| | - Fang Yin
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Linsheng Huang
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lulu Ding
- Qidong People's Hospital/Qidong Liver Cancer Institute, Qidong, China
| | - Xiaohui Zhang
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yonghui Zhang
- Qidong People's Hospital/Qidong Liver Cancer Institute, Qidong, China
| | - Xiao Xiong
- Realbio Genomics Institute, Shanghai, China
| | | | - Xiang Chen
- Realbio Genomics Institute, Shanghai, China
| | - Yefei Zhu
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lin Liu
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yongqiang Liu
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yongshen Chen
- Qidong People's Hospital/Qidong Liver Cancer Institute, Qidong, China
| | - Jian Fan
- Qidong People's Hospital/Qidong Liver Cancer Institute, Qidong, China
| | - Yan Sun
- Qidong People's Hospital/Qidong Liver Cancer Institute, Qidong, China
| | - Jun Wang
- Qidong People's Hospital/Qidong Liver Cancer Institute, Qidong, China
| | - Zhan Cao
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chunsun Fan
- Qidong People's Hospital/Qidong Liver Cancer Institute, Qidong, China
| | - S Dusko Ehrlich
- MGP MetaGenoPolis, INRAE, Université Paris-Saclay, Jouy en Josas, France
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy
| | - Nan Qin
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
- Realbio Genomics Institute, Shanghai, China.
| | - Huanlong Qin
- Institute of Intestinal Diseases, Department of General Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
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24
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Hang D, Zeleznik OA, Lu J, Joshi AD, Wu K, Hu Z, Shen H, Clish CB, Liang L, Eliassen AH, Ogino S, Meyerhardt JA, Chan AT, Song M. Plasma metabolomic profiles for colorectal cancer precursors in women. Eur J Epidemiol 2022; 37:413-422. [PMID: 35032257 PMCID: PMC9189062 DOI: 10.1007/s10654-021-00834-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/17/2021] [Indexed: 01/26/2023]
Abstract
How metabolome changes influence the early process of colorectal cancer (CRC) development remains unknown. We conducted a 1:2 matched nested case-control study to examine the associations of pre-diagnostic plasma metabolome (profiled using LC-MS) with risk of CRC precursors, including conventional adenomas (n = 586 vs. 1141) and serrated polyps (n = 509 vs. 993), in the Nurses' Health Study (NHS) and NHSII. Conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). We used the permutation-based Westfall and Young approach to account for multiple testing. Subgroup analyses were performed for advanced conventional adenomas (defined as at least one adenoma of ≥ 10 mm or with high-grade dysplasia, or tubulovillous or villous histology) and high-risk serrated polyps that were located in the proximal colon or with size of ≥ 10 mm. After multiple testing correction, among 207 metabolites, higher levels of C36:3 phosphatidylcholine (PC) plasmalogen were associated with lower risk of conventional adenomas, with the OR (95% CI) comparing the 90th to the 10th percentile of 0.62 (0.48-0.81); C54:8 triglyceride (TAG) was associated with higher risk of serrated polyps (OR = 1.79, 95% CI: 1.31-2.43), and phenylacetylglutamine (PAG) was associated with lower risk (OR = 0.57, 95% CI:0.43-0.77). PAG was also inversely associated with advanced adenomas (OR = 0.57, 95% CI: 0.36-0.89) and high-risk serrated polyps (OR = 0.54, 95% CI: 0.32-0.89), although the multiple testing-corrected p value was > 0.05. Our findings suggest potential roles of lipid metabolism and phenylacetylglutamine, a microbial metabolite, in the early stage of colorectal carcinogenesis, particularly for the serrated pathway.
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Affiliation(s)
- Dong Hang
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 667 Huntington Avenue, Kresge 906A, Boston, MA, 02115, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jiayi Lu
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kana Wu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 667 Huntington Avenue, Kresge 906A, Boston, MA, 02115, USA
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Shuji Ogino
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Dana-Farber Harvard Cancer Center, Cancer Immunology Program, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | | | - Andrew T Chan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 667 Huntington Avenue, Kresge 906A, Boston, MA, 02115, USA.
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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25
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Age- and Diet-Dependent Changes in Hepatic Lipidomic Profiles of Phospholipids in Male Mice: Age Acceleration in Cyp2b-Null Mice. J Lipids 2022; 2022:7122738. [PMID: 35391786 PMCID: PMC8983274 DOI: 10.1155/2022/7122738] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 03/03/2022] [Indexed: 11/17/2022] Open
Abstract
Increases in traditional serum lipid profiles are associated with obesity, cancer, and cardiovascular disease. Recent lipidomic analysis has indicated changes in serum lipidome profiles, especially in regard to specific phosphatidylcholines, associated with obesity. However, little work has evaluated murine hepatic liver lipidomic profiles nor compared these profiles across age, high-fat diet, or specific genotypes, in this case the lack of hepatic Cyp2b enzymes. In this study, the effects of age (9 months old), high-fat diet (4.5 months old), and the loss of three primarily hepatic xeno- and endobiotic metabolizing cytochrome P450 (Cyp) enzymes, Cyp2b9, Cyp2b10, and Cyp2b13 (Cyp2b-null mice), on the male murine hepatic lipidome were compared. Hierarchical clustering and principal component analysis show that age perturbs hepatic phospholipid profiles and serum lipid markers the most compared to young mice, followed by a high-fat diet and then loss of Cyp2b. Several lipid biomarkers such as PC/PE ratios, PE 38 : 6, and LPC concentrations indicate greater potential for NAFLD and hypertension with mixed effects in Cyp2b-null mice(less NAFLD and greater hypertension-associated markers). Lipid profiles from older mice contain greater total and n-6 fatty acids than normal diet (ND)-fed young mice; however, surprisingly, young Cyp2b-null mice contain high n-6 : n-3 ratios. Overall, the lack of Cyp2b typically enhanced adverse physiological parameters observed in the older (9 mo) mice with increased weight gain combined with a deteriorating cholesterol profile, but not necessarily all phospholipid profiles were adversely perturbed.
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26
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Hamsanathan S, Anthonymuthu T, Han S, Shinglot H, Siefken E, Sims A, Sen P, Pepper HL, Snyder NW, Bayir H, Kagan V, Gurkar AU. Integrated -omics approach reveals persistent DNA damage rewires lipid metabolism and histone hyperacetylation via MYS-1/Tip60. SCIENCE ADVANCES 2022; 8:eabl6083. [PMID: 35171671 PMCID: PMC8849393 DOI: 10.1126/sciadv.abl6083] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
Although DNA damage is intricately linked to metabolism, the metabolic alterations that occur in response to DNA damage are not well understood. We use a DNA repair-deficient model of ERCC1-XPF in Caenorhabditis elegans to gain insights on how genotoxic stress drives aging. Using multi-omic approach, we discover that nuclear DNA damage promotes mitochondrial β-oxidation and drives a global loss of fat depots. This metabolic shift to β-oxidation generates acetyl-coenzyme A to promote histone hyperacetylation and an associated change in expression of immune-effector and cytochrome genes. We identify the histone acetyltransferase MYS-1, as a critical regulator of this metabolic-epigenetic axis. We show that in response to DNA damage, polyunsaturated fatty acids, especially arachidonic acid (AA) and AA-related lipid mediators, are elevated and this is dependent on mys-1. Together, these findings reveal that DNA damage alters the metabolic-epigenetic axis to drive an immune-like response that can promote age-associated decline.
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Affiliation(s)
- Shruthi Hamsanathan
- Aging Institute of UPMC and the University of Pittsburgh School of Medicine, 100 Technology Dr., Pittsburgh, PA 15219, USA
| | - Tamil Anthonymuthu
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
- Children’s Neuroscience Institute, Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA 15224, USA
- Adeptrix Corp., Beverly, MA 01915, USA
| | - Suhao Han
- Aging Institute of UPMC and the University of Pittsburgh School of Medicine, 100 Technology Dr., Pittsburgh, PA 15219, USA
| | - Himaly Shinglot
- Aging Institute of UPMC and the University of Pittsburgh School of Medicine, 100 Technology Dr., Pittsburgh, PA 15219, USA
| | - Ella Siefken
- Aging Institute of UPMC and the University of Pittsburgh School of Medicine, 100 Technology Dr., Pittsburgh, PA 15219, USA
| | - Austin Sims
- Aging Institute of UPMC and the University of Pittsburgh School of Medicine, 100 Technology Dr., Pittsburgh, PA 15219, USA
| | - Payel Sen
- Laboratory of Genetics and Genomics, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Hannah L. Pepper
- Center for Metabolic Disease Research, Department of Cardiovascular Sciences, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Nathaniel W. Snyder
- Center for Metabolic Disease Research, Department of Cardiovascular Sciences, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Hulya Bayir
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
- Children’s Neuroscience Institute, Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA 15224, USA
- Department of Environmental Occupational Health, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Valerian Kagan
- Children’s Neuroscience Institute, Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA 15224, USA
- Department of Environmental Occupational Health, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Aditi U. Gurkar
- Aging Institute of UPMC and the University of Pittsburgh School of Medicine, 100 Technology Dr., Pittsburgh, PA 15219, USA
- Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh School of Medicine, 3471 Fifth Avenue, Kaufmann Medical Building Suite 500, Pittsburgh, PA 15213, USA
- Geriatric Research, Education and Clinical Center, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA 15240, USA
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A multi-omics study of circulating phospholipid markers of blood pressure. Sci Rep 2022; 12:574. [PMID: 35022422 PMCID: PMC8755711 DOI: 10.1038/s41598-021-04446-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/13/2021] [Indexed: 01/02/2023] Open
Abstract
High-throughput techniques allow us to measure a wide-range of phospholipids which can provide insight into the mechanisms of hypertension. We aimed to conduct an in-depth multi-omics study of various phospholipids with systolic blood pressure (SBP) and diastolic blood pressure (DBP). The associations of blood pressure and 151 plasma phospholipids measured by electrospray ionization tandem mass spectrometry were performed by linear regression in five European cohorts (n = 2786 in discovery and n = 1185 in replication). We further explored the blood pressure-related phospholipids in Erasmus Rucphen Family (ERF) study by associating them with multiple cardiometabolic traits (linear regression) and predicting incident hypertension (Cox regression). Mendelian Randomization (MR) and phenome-wide association study (Phewas) were also explored to further investigate these association results. We identified six phosphatidylethanolamines (PE 38:3, PE 38:4, PE 38:6, PE 40:4, PE 40:5 and PE 40:6) and two phosphatidylcholines (PC 32:1 and PC 40:5) which together predicted incident hypertension with an area under the ROC curve (AUC) of 0.61. The identified eight phospholipids are strongly associated with triglycerides, obesity related traits (e.g. waist, waist-hip ratio, total fat percentage, body mass index, lipid-lowering medication, and leptin), diabetes related traits (e.g. glucose, insulin resistance and insulin) and prevalent type 2 diabetes. The genetic determinants of these phospholipids also associated with many lipoproteins, heart rate, pulse rate and blood cell counts. No significant association was identified by bi-directional MR approach. We identified eight blood pressure-related circulating phospholipids that have a predictive value for incident hypertension. Our cross-omics analyses show that phospholipid metabolites in the circulation may yield insight into blood pressure regulation and raise a number of testable hypothesis for future research.
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Al-Muraikhy S, Sellami M, Domling AS, Rizwana N, Agouni A, Al-Khelaifi F, Donati F, Botre F, Diboun I, Elrayess MA. Metabolic Signature of Leukocyte Telomere Length in Elite Male Soccer Players. Front Mol Biosci 2021; 8:727144. [PMID: 34977149 PMCID: PMC8716766 DOI: 10.3389/fmolb.2021.727144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 12/02/2021] [Indexed: 11/23/2022] Open
Abstract
Introduction: Biological aging is associated with changes in the metabolic pathways. Leukocyte telomere length (LTL) is a predictive marker of biological aging; however, the underlying metabolic pathways remain largely unknown. The aim of this study was to investigate the metabolic alterations and identify the metabolic predictors of LTL in elite male soccer players. Methods: Levels of 837 blood metabolites and LTL were measured in 126 young elite male soccer players who tested negative for doping abuse at anti-doping laboratory in Italy. Multivariate analysis using orthogonal partial least squares (OPLS), univariate linear models and enrichment analyses were conducted to identify metabolites and metabolic pathways associated with LTL. Generalized linear model followed by receiver operating characteristic (ROC) analysis were conducted to identify top metabolites predictive of LTL. Results: Sixty-seven metabolites and seven metabolic pathways showed significant associations with LTL. Among enriched pathways, lysophospholipids, benzoate metabolites, and glycine/serine/threonine metabolites were elevated with longer LTL. Conversely, monoacylglycerols, sphingolipid metabolites, long chain fatty acids and polyunsaturated fatty acids were enriched with shorter telomeres. ROC analysis revealed eight metabolites that best predict LTL, including glutamine, N-acetylglutamine, xanthine, beta-sitosterol, N2-acetyllysine, stearoyl-arachidonoyl-glycerol (18:0/20:4), N-acetylserine and 3-7-dimethylurate with AUC of 0.75 (0.64-0.87, p < 0.0001). Conclusion: This study characterized the metabolic activity in relation to telomere length in elite soccer players. Investigating the functional relevance of these associations could provide a better understanding of exercise physiology and pathophysiology of elite athletes.
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Affiliation(s)
- Shamma Al-Muraikhy
- Biomedical Research Center, Qatar University, Doha, Qatar
- Department of Drug Design, University of Groningen, Groningen, Netherlands
| | - Maha Sellami
- Department of Physical Education (PE), College of Education, Qatar University, Doha, Qatar
| | | | - Najeha Rizwana
- Biomedical Research Center, Qatar University, Doha, Qatar
| | - Abdelali Agouni
- Department of Pharmaceutical Sciences, College of Pharmacy, QU Health, Qatar University, Doha, Qatar
- Biomedical and Pharmaceutical Research Unit (BPRU), QU Health, Qatar University, Doha, Qatar
| | | | - Francesco Donati
- Laboratorio Antidoping, Federazione Medico Sportiva Italiana, Rome, Italy
| | - Francesco Botre
- Laboratorio Antidoping, Federazione Medico Sportiva Italiana, Rome, Italy
| | - Ilhame Diboun
- College of Health and Life Sciences, Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Mohamed A Elrayess
- Biomedical Research Center, Qatar University, Doha, Qatar
- Biomedical and Pharmaceutical Research Unit (BPRU), QU Health, Qatar University, Doha, Qatar
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29
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Pradas I, Jové M, Huynh K, Ingles M, Borras C, Mota-Martorell N, Galo-Licona JD, Puig J, Viña J, Meikle PJ, Pamplona R. Long-lived humans have a unique plasma sphingolipidome. J Gerontol A Biol Sci Med Sci 2021; 77:728-735. [PMID: 34871393 PMCID: PMC8974335 DOI: 10.1093/gerona/glab360] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Indexed: 11/12/2022] Open
Abstract
A species-specific lipidome profile is an inherent feature linked to longevity in the animal kingdom. However, there is a lack of lipidomic studies on human longevity. Here, we use mass spectrometry-based lipidomics to detect and quantify 151 sphingolipid molecular species and use these to define a phenotype of healthy humans with exceptional life span. Our results demonstrate that this profile specifically comprises a higher content of complex glycosphingolipids (hexosylceramides and gangliosides), and lower levels of ceramide species from the de novo pathway, sphingomyelin and sulfatide; while for ceramide-derived signaling compounds, their content remains unchanged. Our findings suggest that structural glycosphingolipids may be more relevant to achieve the centenarian condition than signaling sphingolipids.
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Affiliation(s)
- Irene Pradas
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida 25198, Catalonia, Spain
| | - Mariona Jové
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida 25198, Catalonia, Spain
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne VIC 3004, Australia
| | - Marta Ingles
- Department of Physiology, University of Valencia, Valencia 46004, Spain
| | - Consuelo Borras
- Department of Physiology, University of Valencia, Valencia 46004, Spain
| | - Natalia Mota-Martorell
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida 25198, Catalonia, Spain
| | - Jose Daniel Galo-Licona
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida 25198, Catalonia, Spain
| | - Josep Puig
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari Dr Josep Trueta, Girona 17007, Catalonia, Spain
| | - Jose Viña
- Department of Physiology, University of Valencia, Valencia 46004, Spain
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne VIC 3004, Australia
| | - Reinald Pamplona
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida 25198, Catalonia, Spain
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30
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Abstract
Background Cardiorespiratory fitness (CRF) is a potent health marker, the improvement of which is associated with a reduced incidence of non-communicable diseases and all-cause mortality. Identifying metabolic signatures associated with CRF could reveal how CRF fosters human health and lead to the development of novel health-monitoring strategies. Objective This article systematically reviewed reported associations between CRF and metabolites measured in human tissues and body fluids. Methods PubMed, EMBASE, and Web of Science were searched from database inception to 3 June, 2021. Metabolomics studies reporting metabolites associated with CRF, measured by means of cardiopulmonary exercise test, were deemed eligible. Backward and forward citation tracking on eligible records were used to complement the results of database searching. Risk of bias at the study level was assessed using QUADOMICS. Results Twenty-two studies were included and 667 metabolites, measured in plasma (n = 619), serum (n = 18), skeletal muscle (n = 16), urine (n = 11), or sweat (n = 3), were identified. Lipids were the metabolites most commonly positively (n = 174) and negatively (n = 274) associated with CRF. Specific circulating glycerophospholipids (n = 85) and cholesterol esters (n = 17) were positively associated with CRF, while circulating glycerolipids (n = 152), glycerophospholipids (n = 42), acylcarnitines (n = 14), and ceramides (n = 12) were negatively associated with CRF. Interestingly, muscle acylcarnitines were positively correlated with CRF (n = 15). Conclusions Cardiorespiratory fitness was associated with circulating and muscle lipidome composition. Causality of the revealed associations at the molecular species level remains to be investigated further. Finally, included studies were heterogeneous in terms of participants’ characteristics and analytical and statistical approaches. PROSPERO Registration Number CRD42020214375. Supplementary Information The online version contains supplementary material available at 10.1007/s40279-021-01590-y.
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31
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Wang Y, Huang Y, Wu P, Ye Y, Sun F, Yang X, Lu Q, Yuan J, Liu Y, Zeng H, Song X, Yan S, Qi X, Yang CX, Lv C, Wu JHY, Liu G, Pan XF, Chen D, Pan A. Plasma lipidomics in early pregnancy and risk of gestational diabetes mellitus: a prospective nested case-control study in Chinese women. Am J Clin Nutr 2021; 114:1763-1773. [PMID: 34477820 DOI: 10.1093/ajcn/nqab242] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 06/28/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Lipid metabolism plays an important role in the pathogenesis of diabetes. There is little evidence regarding the prospective association of the maternal lipidome with gestational diabetes mellitus (GDM), especially in Chinese populations. OBJECTIVES We aimed to identify novel lipid species associated with GDM risk in Chinese women, and assess the incremental predictive capacity of the lipids for GDM. METHODS We conducted a nested case-control study using the Tongji-Shuangliu Birth Cohort with 336 GDM cases and 672 controls, 1:2 matched on age and week of gestation. Maternal blood samples were collected at 6-15 wk, and lipidomes were profiled by targeted ultra-HPLC-tandem MS. GDM was diagnosed by oral-glucose-tolerance test at 24-28 wk. The least absolute shrinkage and selection operator is a regression analysis method that was used to select novel biomarkers. Multivariable conditional logistic regression was used to estimate the associations. RESULTS Of 366 detected lipids, 10 were selected and found to be significantly associated with GDM independently of confounders: there were positive associations with phosphatidylinositol 40:6, alkylphosphatidylcholine 36:1, phosphatidylethanolamine plasmalogen 38:6, diacylglyceride 18:0/18:1, and alkylphosphatidylethanolamine 40:5 (adjusted ORs per 1 log-SD increment range: 1.34-2.86), whereas there were inverse associations with sphingomyelin 34:1, dihexosyl ceramide 24:0, mono hexosyl ceramide 18:0, dihexosyl ceramide 24:1, and phosphatidylcholine 40:7 (adjusted ORs range: 0.48-0.68). Addition of these novel lipids to the classical GDM prediction model resulted in a significant improvement in the C-statistic (discriminatory power of the model) to 0.801 (95% CI: 0.772, 0.829). For every 1-point increase in the lipid risk score of the 10 lipids, the OR of GDM was 1.66 (95% CI: 1.50, 1.85). Mediation analysis suggested the associations between specific lipid species and GDM were partially explained by glycemic and insulin-related indicators. CONCLUSIONS Specific plasma lipid biomarkers in early pregnancy were associated with GDM in Chinese women, and significantly improved the prediction for GDM.
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Affiliation(s)
- Yi Wang
- Department of Epidemiology & Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.,Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yichao Huang
- School of Environment, Guangdong Key Laboratory of Environmental Pollution and Health, Jinan University, Guangzhou, Guangdong, China
| | - Ping Wu
- Department of Epidemiology & Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.,Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yi Ye
- Department of Epidemiology & Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.,Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Fengjiang Sun
- School of Environment, Guangdong Key Laboratory of Environmental Pollution and Health, Jinan University, Guangzhou, Guangdong, China
| | - Xue Yang
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qi Lu
- Department of Epidemiology & Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.,Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiaying Yuan
- Department of Science and Education, Shuangliu Maternal and Child Health Hospital, Chengdu, Sichuan, China
| | - Yan Liu
- Department of Obstetrics and Gynecology, Shuangliu Maternal and Child Health Hospital, Chengdu, Sichuan, China
| | - Huayan Zeng
- Nutrition Department, Shuangliu Maternal and Child Health Hospital, Chengdu, Sichuan, China
| | - Xingyue Song
- Department of Emergency, Hainan Clinical Research Center for Acute and Critical Diseases, The Second Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China.,Key Laboratory of Emergency and Trauma of Ministry of Education, Hainan Medical University, Haikou, Hainan, China
| | - Shijiao Yan
- Research Unit of Island Emergency Medicine, Chinese Academy of Medical Sciences, Hainan Medical University, Haikou, Hainan, China.,School of Public Health, Hainan Medical University, Haikou, Hainan, China
| | - Xiaorong Qi
- Department of Gynecology and Obstetrics, West China Second Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, Sichuan, China
| | - Chun-Xia Yang
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chuanzhu Lv
- Department of Emergency, Hainan Clinical Research Center for Acute and Critical Diseases, The Second Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China.,Key Laboratory of Emergency and Trauma of Ministry of Education, Hainan Medical University, Haikou, Hainan, China.,Research Unit of Island Emergency Medicine, Chinese Academy of Medical Sciences, Hainan Medical University, Haikou, Hainan, China
| | - Jason H Y Wu
- The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Gang Liu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiong-Fei Pan
- Department of Epidemiology & Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.,Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.,The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia.,Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Da Chen
- School of Environment, Guangdong Key Laboratory of Environmental Pollution and Health, Jinan University, Guangzhou, Guangdong, China
| | - An Pan
- Department of Epidemiology & Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.,Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Berdún R, Jové M, Sol J, Cai W, He JC, Rodriguez-Mortera R, Martin-Garí M, Pamplona R, Uribarri J, Portero-Otin M. Restriction of Dietary Advanced Glycation End Products Induces a Differential Plasma Metabolome and Lipidome Profile. Mol Nutr Food Res 2021; 65:e2000499. [PMID: 34599622 DOI: 10.1002/mnfr.202000499] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 02/10/2021] [Indexed: 12/30/2022]
Abstract
SCOPE Diets with low content in advanced glycation end products (AGEs) lead to beneficial properties in highly prevalent age-related diseases. To shed light on the mechanisms behind, the changes induced by a low AGE dietary intervention in the circulating metabolome are analyzed. METHODS AND RESULTS To this end, 20 non-diabetic patients undergoing peritoneal dialysis are randomized to continue their usual diet or to one with a low content of AGEs for 1 month. Then, plasmatic metabolome and lipidomes are analyzed by liquid-chromatography coupled to mass spectrometry. The levels of defined AGE structures are also quantified by ELISA and by mass-spectrometry. The results show that the low AGE diet impinged significant changes in circulating metabolomes (166 molecules) and lipidomes (91 lipids). Metabolic targets of low-AGE intake include sphingolipid, ether-lipids, and glycerophospholipid metabolism. Further, it reproduces some of the plasma characteristics of healthy aging. CONCLUSION The finding of common pathways induced by low-AGE diets with previous metabolic traits implicated in aging, insulin resistance, and obesity suggest the usefulness of the chosen approach and supports the potential extension of this study to other populations.
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Affiliation(s)
- Rebeca Berdún
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Mariona Jové
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Joaquim Sol
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain.,Primary Care, Catalan Health Institute (ICS), Lleida, Spain.,Research Support Unit Lleida, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Lleida, Spain
| | - Weijing Cai
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - John C He
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Reyna Rodriguez-Mortera
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Meritxell Martin-Garí
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Reinald Pamplona
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Jaime Uribarri
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Manuel Portero-Otin
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
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33
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Zhou Y, Hu G, Wang MC. Host and microbiota metabolic signals in aging and longevity. Nat Chem Biol 2021; 17:1027-1036. [PMID: 34552221 DOI: 10.1038/s41589-021-00837-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 06/16/2021] [Indexed: 02/07/2023]
Abstract
Aging is an inevitable biochemical process that adversely affects personal health and poses ever-increasing challenges to society. Recent research has revealed the crucial role of metabolism in regulating aging and longevity. During diverse metabolic processes, the host organism and their symbiotic partners-the microbiota-produce thousands of chemical products (metabolites). Emerging studies have uncovered specific metabolites that act as signaling molecules to actively regulate longevity. Here we review the latest progress in understanding the molecular mechanisms by which metabolites from the host and/or microbiota promote longevity. We also highlight state-of-the-art technologies for discovering, profiling and imaging aging- and longevity-regulating metabolites and for deciphering the molecular basis of their actions. The broad application of these technologies in aging research, together with future advances, will foster the systematic discovery of aging- and longevity-regulating metabolites and their signaling pathways. These metabolite signals should provide promising targets for developing new interventions to promote longevity and healthy aging.
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Affiliation(s)
- Yue Zhou
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
| | - Guo Hu
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA.,Graduate Program in Genetics and Genomics, Baylor College of Medicine, Houston, TX, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Meng C Wang
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA. .,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. .,Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX, USA.
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Erkkilä AT, Manninen S, Fredrikson L, Bhalke M, Holopainen M, Ruuth M, Lankinen M, Käkelä R, Öörni K, Schwab US. Lipidomic changes of LDL after consumption of Camelina sativa oil, fatty fish and lean fish in subjects with impaired glucose metabolism-A randomized controlled trial. J Clin Lipidol 2021; 15:743-751. [PMID: 34548243 DOI: 10.1016/j.jacl.2021.08.060] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 08/30/2021] [Accepted: 08/30/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND There is little knowledge on the effects of alpha-linolenic acid (ALA) and n-3 long-chain polyunsaturated fatty acids (n-3 LCPUFA) on the LDL lipidome and aggregation of LDL particles. OBJECTIVE We examined if consumption of Camelina sativa oil (CSO) as a source of ALA, fatty fish (FF) as a source of n-3 LCPUFA and lean fish (LF) as a source of fish protein affect the lipidome of LDL as compared to a control diet. METHODS Participants with impaired glucose tolerance (39 women and 40 men) were randomized to 4 study groups (CSO providing 10 g/d ALA, FF and LF [both 4 fish meals/wk] and control limiting their fish and ALA intake) in a 12-week, parallel trial. Diets were instructed and dietary fats were provided to the participants. The lipidome of LDL particles isolated from samples collected at baseline and after intervention was analyzed with electrospray ionization-tandem mass spectrometry. RESULTS In the CSO group, the relative concentrations of saturated and monounsaturated cholesteryl ester species in LDL decreased and the species with ALA increased. In the FF group, LDL phosphatidylcholine (PC) species containing n-3 LCPUFA increased. There was a significant positive correlation between the change in total sphingomyelin and change in LDL aggregation, while total PC and triunsaturated PC species were inversely associated with LDL aggregation when all the study participants were included in the analysis. CONCLUSION Dietary intake of CSO and FF modifies the LDL lipidome to contain more polyunsaturated and less saturated lipid species. The LDL surface lipids are associated with LDL aggregation.
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Affiliation(s)
- Arja T Erkkilä
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland.
| | - Suvi Manninen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Linda Fredrikson
- Helsinki University Lipidomics Unit (HiLIPID), Helsinki Institute for Life Sciences (HiLIFE) and Biocenter Finland, University of Helsinki, Helsinki, Finland; Molecular and Integrative Biosciences Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Monika Bhalke
- Helsinki University Lipidomics Unit (HiLIPID), Helsinki Institute for Life Sciences (HiLIFE) and Biocenter Finland, University of Helsinki, Helsinki, Finland; Molecular and Integrative Biosciences Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Minna Holopainen
- Helsinki University Lipidomics Unit (HiLIPID), Helsinki Institute for Life Sciences (HiLIFE) and Biocenter Finland, University of Helsinki, Helsinki, Finland; Molecular and Integrative Biosciences Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Maija Ruuth
- Atherosclerosis Research Laboratory, Wihuri Research Institute, Helsinki, Finland; Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Maria Lankinen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Reijo Käkelä
- Helsinki University Lipidomics Unit (HiLIPID), Helsinki Institute for Life Sciences (HiLIFE) and Biocenter Finland, University of Helsinki, Helsinki, Finland; Molecular and Integrative Biosciences Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Katariina Öörni
- Atherosclerosis Research Laboratory, Wihuri Research Institute, Helsinki, Finland; Molecular and Integrative Biosciences Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Ursula S Schwab
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; Department of Medicine, Endocrinology and Clinical Nutrition, Kuopio University Hospital, Kuopio, Finland
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35
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Comprehensive metabolomics profiling reveals common metabolic alterations underlying the four major non-communicable diseases in treated HIV infection. EBioMedicine 2021; 71:103548. [PMID: 34419928 PMCID: PMC8385138 DOI: 10.1016/j.ebiom.2021.103548] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/04/2021] [Accepted: 08/06/2021] [Indexed: 12/14/2022] Open
Abstract
Background HIV infection and normal aging share immune and inflammatory changes that result in premature aging and non-communicable diseases (NCDs), but the exact pathophysiology is not yet uncovered. We identified the common metabolic pathways underlying various NCDs in treated HIV infection. Methods We performed untargeted metabolomics including 87 HIV-negative (–) normal controls (NCs), 87 HIV-positive (+) NCs, and 148 HIV+ subjects with only one type of NCDs, namely, subclinical carotid atherosclerosis, neurocognitive impairment (NCI), liver fibrosis (LF) and renal impairment. All HIV+ subjects were virally suppressed. Results HIV+ patients presented widespread alterations in cellular metabolism compared to HIV– NCs. Glycerophospholipid (GPL) metabolism was the only one disturbed pathway presented in comparisons including HIV– NCs across age groups, HIV+ NCs across age groups, HIV+ NCs vs HIV– NCs and each of HIV+ NCDs vs HIV+ NCs. D-glutamine and D-glutamate metabolism and alanine-aspartate-glutamate metabolism were presented in comparisons between HIV+ NCs vs HIV– NCs, HIV+ LF or HIV+ NCI vs HIV+ NCs. Consistently, subsequent analysis identified a metabolomic fingerprint specific for HIV+ NCDs, containing 42 metabolites whose relative abundance showed either an upward (mainly GPL-derived lipid mediators) or a downward trend (mainly plasmalogen phosphatidylcholines, plasmalogen phosphatidylethanolamines, and glutamine) from HIV– NCs to HIV+ NCs and then HIV+ NCDs, reflecting a trend of increased oxidative stress. Interpretation GPL metabolism emerges as the common metabolic disturbance linking HIV to NCDs, followed by glutamine and glutamate metabolism. Together, our data point to the aforementioned metabolisms and related metabolites as potential key targets in studying pathophysiology of NCDs in HIV infection and developing therapeutic interventions. Funding China National Science and Technology Major Projects on Infectious Diseases, National Natural Science Foundation of China, Yi-wu Institute of Fudan University, and Shanghai Municipal Health and Family Planning Commission.
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36
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Di Cesare F, Tenori L, Meoni G, Gori AM, Marcucci R, Giusti B, Molino-Lova R, Macchi C, Pancani S, Luchinat C, Saccenti E. Lipid and metabolite correlation networks specific to clinical and biochemical covariate show differences associated with sexual dimorphism in a cohort of nonagenarians. GeroScience 2021; 44:1109-1128. [PMID: 34324142 PMCID: PMC9135919 DOI: 10.1007/s11357-021-00404-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 06/13/2021] [Indexed: 12/26/2022] Open
Abstract
This study defines and estimates the metabolite-lipidic component association networks constructed from an array of 20 metabolites and 114 lipids identified and quantified via NMR spectroscopy in the serum of a cohort of 355 Italian nonagenarians and ultra-nonagenarian. Metabolite-lipid association networks were built for men and women and related to an array of 101 clinical and biochemical parameters, including the presence of diseases, bio-humoral parameters, familiarity diseases, drugs treatments, and risk factors. Different connectivity patterns were observed in lipids, branched chains amino acids, alanine, and ketone bodies, suggesting their association with the sex-related and sex-clinical condition-related intrinsic metabolic changes. Furthermore, our results demonstrate, using a holistic system biology approach, that the characterization of metabolic structures and their dynamic inter-connections is a promising tool to shed light on the dimorphic pathophysiological mechanisms of aging at the molecular level.
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Affiliation(s)
- Francesca Di Cesare
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | | | - Anna Maria Gori
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Atherothrombotic Unit, Careggi University Hospital, Florence, Italy
| | - Rossella Marcucci
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Atherothrombotic Unit, Careggi University Hospital, Florence, Italy
| | - Betti Giusti
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Atherothrombotic Unit, Careggi University Hospital, Florence, Italy
| | | | - Claudio Macchi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | | | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy.,Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
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37
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Mohammadzadeh Honarvar N, Zarezadeh M, Molsberry SA, Ascherio A. Changes in plasma phospholipids and sphingomyelins with aging in men and women: A comprehensive systematic review of longitudinal cohort studies. Ageing Res Rev 2021; 68:101340. [PMID: 33839333 DOI: 10.1016/j.arr.2021.101340] [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: 10/28/2020] [Revised: 02/25/2021] [Accepted: 04/02/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Aging affects the serum levels of various metabolites which may be involved in the pathogenesis of chronic diseases. The aim of this review article is to summarize the relationship between aging and alterations in the plasma phospholipids and sphingomyelins. METHODS PRISMA guidelines were employed during all steps. MEDLINE (PubMed), Scopus, Embase and Web of Sciences databases and Google Scholar were searched up to October 2020. Cohort studies investigating the relationship between aging and within-person changes in sphingomyelin (SM), phosphatidyl choline (PC), lyso PC (LPC) and phosphatidyl ethanolamine (PE) were included. Newcastle-Ottawa scale was used to assess the quality of included studies. RESULTS A total of 1425 studies were identified. After removing 610 duplicates and 723 irrelevant studies, full texts of 92 articles were evaluated. Of these 92, 6 studies (including data from 7 independent cohorts) met the inclusion criteria and are included in this review. All study populations were healthy and included both men and women. Results by sex were reported in 3 cohorts for PC, 5 cohorts for LPC, 3 cohorts for SM, and only 1 cohort for PE. In men, PC, SM, PE and LPC decreased with aging, although results for LPC were inconsistent. In women, LPC, SM, and PE increased age, whereas changes in PC were inconsistent. CONCLUSION Within-person serum levels of phospholipids and sphingomyelins, decrease during aging in men and increase in women. Notably, however, there were some inconsistencies across studies of LPC in men and of PC in women.
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38
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Wu Q, Yu X, Li J, Sun S, Tu Y. Metabolic regulation in the immune response to cancer. Cancer Commun (Lond) 2021; 41:661-694. [PMID: 34145990 PMCID: PMC8360644 DOI: 10.1002/cac2.12182] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 03/25/2021] [Accepted: 06/11/2021] [Indexed: 02/06/2023] Open
Abstract
Metabolic reprogramming in tumor‐immune interactions is emerging as a key factor affecting pro‐inflammatory carcinogenic effects and anticancer immune responses. Therefore, dysregulated metabolites and their regulators affect both cancer progression and therapeutic response. Here, we describe the molecular mechanisms through which microenvironmental, systemic, and microbial metabolites potentially influence the host immune response to mediate malignant progression and therapeutic intervention. We summarized the primary interplaying factors that constitute metabolism, immunological reactions, and cancer with a focus on mechanistic aspects. Finally, we discussed the possibility of metabolic interventions at multiple levels to enhance the efficacy of immunotherapeutic and conventional approaches for future anticancer treatments.
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Affiliation(s)
- Qi Wu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, P. R. China
| | - Xin Yu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, P. R. China
| | - Juanjuan Li
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, P. R. China
| | - Shengrong Sun
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, P. R. China
| | - Yi Tu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, P. R. China
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39
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Zhu D, Montagne A, Zhao Z. Alzheimer's pathogenic mechanisms and underlying sex difference. Cell Mol Life Sci 2021; 78:4907-4920. [PMID: 33844047 PMCID: PMC8720296 DOI: 10.1007/s00018-021-03830-w] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/09/2021] [Accepted: 03/29/2021] [Indexed: 01/01/2023]
Abstract
AD is a neurodegenerative disease, and its frequency is often reported to be higher for women than men: almost two-thirds of patients with AD are women. One prevailing view is that women live longer than men on average of 4.5 years, plus there are more women aged 85 years or older than men in most global subpopulations; and older age is the greatest risk factor for AD. However, the differences in the actual risk of developing AD for men and women of the same age is difficult to assess, and the findings have been mixed. An increasing body of evidence from preclinical and clinical studies as well as the complications in estimating incidence support the sex-specific biological mechanisms in diverging AD risk as an important adjunct explanation to the epidemiologic perspective. Although some of the sex differences in AD prevalence are due to differences in longevity, other distinct biological mechanisms increase the risk and progression of AD in women. These risk factors include (1) deviations in brain structure and biomarkers, (2) psychosocial stress responses, (3) pregnancy, menopause, and sex hormones, (4) genetic background (i.e., APOE), (5) inflammation, gliosis, and immune module (i.e., TREM2), and (6) vascular disorders. More studies focusing on the underlying biological mechanisms for this phenomenon are needed to better understand AD. This review presents the most recent data in sex differences in AD-the gateway to precision medicine, therefore, shaping expert perspectives, inspiring researchers to go in new directions, and driving development of future diagnostic tools and treatments for AD in a more customized way.
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Affiliation(s)
- Donghui Zhu
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
- Neuroscience Graduate Program, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA.
| | - Axel Montagne
- UK Dementia Research Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Zhen Zhao
- Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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40
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Zhang Y, Xie Y, Lv W, Hu C, Xu T, Liu X, Zhang R, Xu G, Xia Y, Zhao X. A high throughput lipidomics method and its application in atrial fibrillation based on 96-well plate pretreatment and liquid chromatography-mass spectrometry. J Chromatogr A 2021; 1651:462271. [PMID: 34102397 DOI: 10.1016/j.chroma.2021.462271] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/14/2021] [Accepted: 05/18/2021] [Indexed: 11/27/2022]
Abstract
Successful applications of lipidomics in clinic need study large-scale samples, and the bottlenecks are in throughput and robustness of the lipid analytical method. Here, we report an untargeted lipidomics method by combining high throughput pretreatment in the 96-well plate with ultra-high performance liquid chromatography coupled to quadrupole time-of-flight tandem mass spectrometry. The developed method was validated to have satisfactory analytical characteristics in terms of linearity, repeatability and extraction recovery. It can be used to handle 96 samples simultaneously in 25 min and detect 441 lipids in plasma sample. Storage stability investigation on lipid extracts provided an operable procedure for large-scale sample analysis and demonstrated most lipids were stable in autosampler at 10 °C within 36 h and at -80 °C within 72 h after the pretreatment. To prove the usefulness, the method was employed to investigate abnormal plasma lipidome related to atrial fibrillation. A biomarker panel with the area under the curve (AUC) values of 0.831 and 0.745 was achieved in the discovery and external validation sets, respectively. These results showed that the developed method is applicable for large-scale biological sample handling and lipid analysis of plasma.
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Affiliation(s)
- Yuqing Zhang
- Zhang Dayu School of Chemistry, Dalian University of Technology, Dalian 116024, P. R. China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, P. R. China
| | - Yunpeng Xie
- The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wangjie Lv
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, P. R. China
| | - Chunxiu Hu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, P. R. China
| | - Tianrun Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, P. R. China
| | - Xinyu Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, P. R. China
| | - Rongfeng Zhang
- The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Guowang Xu
- Zhang Dayu School of Chemistry, Dalian University of Technology, Dalian 116024, P. R. China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, P. R. China
| | - Yunlong Xia
- The First Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Xinjie Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, P. R. China.
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41
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Carrard J, Gallart-Ayala H, Infanger D, Teav T, Wagner J, Knaier R, Colledge F, Streese L, Königstein K, Hinrichs T, Hanssen H, Ivanisevic J, Schmidt-Trucksäss A. Metabolic View on Human Healthspan: A Lipidome-Wide Association Study. Metabolites 2021; 11:metabo11050287. [PMID: 33946321 PMCID: PMC8146132 DOI: 10.3390/metabo11050287] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 04/23/2021] [Accepted: 04/28/2021] [Indexed: 12/22/2022] Open
Abstract
As ageing is a major risk factor for the development of non-communicable diseases, extending healthspan has become a medical and societal necessity. Precise lipid phenotyping that captures metabolic individuality could support healthspan extension strategies. This study applied ‘omic-scale lipid profiling to characterise sex-specific age-related differences in the serum lipidome composition of healthy humans. A subset of the COmPLETE-Health study, composed of 73 young (25.2 ± 2.6 years, 43% female) and 77 aged (73.5 ± 2.3 years, 48% female) clinically healthy individuals, was investigated, using an untargeted liquid chromatography high-resolution mass spectrometry approach. Compared to their younger counterparts, aged females and males exhibited significant higher levels in 138 and 107 lipid species representing 15 and 13 distinct subclasses, respectively. Percentage of difference ranged from 5.8% to 61.7% (females) and from 5.3% to 46.0% (males), with sphingolipid and glycerophophospholipid species displaying the greatest amplitudes. Remarkably, specific sphingolipid and glycerophospholipid species, previously described as cardiometabolically favourable, were found elevated in aged individuals. Furthermore, specific ether-glycerophospholipid and lyso-glycerophosphocholine species displayed higher levels in aged females only, revealing a more favourable lipidome evolution in females. Altogether, age determined the circulating lipidome composition, while lipid species analysis revealed additional findings that were not observed at the subclass level.
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Affiliation(s)
- Justin Carrard
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Hector Gallart-Ayala
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, CH-1005 Lausanne, Switzerland; (H.G.-A.); (T.T.)
| | - Denis Infanger
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Tony Teav
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, CH-1005 Lausanne, Switzerland; (H.G.-A.); (T.T.)
| | - Jonathan Wagner
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Raphael Knaier
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Flora Colledge
- Division of Sports Science, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland;
| | - Lukas Streese
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Karsten Königstein
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Timo Hinrichs
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Henner Hanssen
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, CH-1005 Lausanne, Switzerland; (H.G.-A.); (T.T.)
- Correspondence: (J.I.); (A.S.-T.)
| | - Arno Schmidt-Trucksäss
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
- Correspondence: (J.I.); (A.S.-T.)
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Mutlu AS, Duffy J, Wang MC. Lipid metabolism and lipid signals in aging and longevity. Dev Cell 2021; 56:1394-1407. [PMID: 33891896 DOI: 10.1016/j.devcel.2021.03.034] [Citation(s) in RCA: 105] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 03/05/2021] [Accepted: 03/29/2021] [Indexed: 02/06/2023]
Abstract
Lipids play crucial roles in regulating aging and longevity. In the past few decades, a series of genetic pathways have been discovered to regulate lifespan in model organisms. Interestingly, many of these regulatory pathways are linked to lipid metabolism and lipid signaling. Lipid metabolic enzymes undergo significant changes during aging and are regulated by different longevity pathways. Lipids also actively modulate lifespan and health span as signaling molecules. In this review, we summarize recent insights into the roles of lipid metabolism and lipid signaling in aging and discuss lipid-related interventions in promoting longevity.
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Affiliation(s)
- Ayse Sena Mutlu
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jonathon Duffy
- Developmental Biology Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - Meng C Wang
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA; Developmental Biology Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX 77030, USA.
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43
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Abstract
Life expectancy, and longevity have been increasing in recent years. However, this is, in most cases, accompanied by age-related diseases. Thus, it became essential to better understand the mechanisms inherent to aging, and to establish biomarkers that characterize this physiological process. Among all biomolecules, lipids appear to be a good target for the study of these biomarkers. In fact, some lipids have already been associated with age-related diseases. With the development of analytical techniques such as Mass Spectrometry, and Nuclear Magnetic Resonance, Lipidomics has been increasingly used to study pathological, and physiological states of an organism. Thus, the study of serum, and plasma lipidome in centenarians, and elderly individuals without age-related diseases can be a useful tool for the identification of aging biomarkers, and to understand physiological aging, and longevity. This review focus on the importance of lipids as biomarkers of aging, and summarize the changes in the lipidome that have been associated with aging, and longevity.
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Mohammad K, Titorenko VI. Caloric restriction creates a metabolic pattern of chronological aging delay that in budding yeast differs from the metabolic design established by two other geroprotectors. Oncotarget 2021; 12:608-625. [PMID: 33868583 PMCID: PMC8021023 DOI: 10.18632/oncotarget.27926] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 03/15/2021] [Indexed: 12/19/2022] Open
Abstract
Caloric restriction and the tor1Δ mutation are robust geroprotectors in yeast and other eukaryotes. Lithocholic acid is a potent geroprotector in Saccharomycescerevisiae. Here, we used liquid chromatography coupled with tandem mass spectrometry method of non-targeted metabolomics to compare the effects of these three geroprotectors on the intracellular metabolome of chronologically aging budding yeast. Yeast cells were cultured in a nutrient-rich medium. Our metabolomic analysis identified and quantitated 193 structurally and functionally diverse water-soluble metabolites implicated in the major pathways of cellular metabolism. We show that the three different geroprotectors create distinct metabolic profiles throughout the entire chronological lifespan of S. cerevisiae. We demonstrate that caloric restriction generates a unique metabolic pattern. Unlike the tor1Δ mutation or lithocholic acid, it slows down the metabolic pathway for sulfur amino acid biosynthesis from aspartate, sulfate and 5-methyltetrahydrofolate. Consequently, caloric restriction significantly lowers the intracellular concentrations of methionine, S-adenosylmethionine and cysteine. We also noticed that the low-calorie diet, but not the tor1Δ mutation or lithocholic acid, decreases intracellular ATP, increases the ADP:ATP and AMP:ATP ratios, and rises intracellular ADP during chronological aging. We propose a model of how the specific remodeling of cellular metabolism by caloric restriction contributes to yeast chronological aging delay.
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Affiliation(s)
- Karamat Mohammad
- Department of Biology, Concordia University, Montreal, Quebec H4B 1R6, Canada
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Lo CJ, Ko YS, Chang SW, Tang HY, Huang CY, Huang YC, Ho HY, Lin CM, Cheng ML. Metabolic signatures of muscle mass loss in an elderly Taiwanese population. Aging (Albany NY) 2020; 13:944-956. [PMID: 33410783 PMCID: PMC7834982 DOI: 10.18632/aging.202209] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 10/05/2020] [Indexed: 12/25/2022]
Abstract
To identify the association between metabolites and muscle mass in 305 elderly Taiwanese subjects, we conducted a multivariate analysis of 153 plasma samples. Based on appendicular skeletal muscle mass index (ASMI) quartiles, female and male participants were divided into four groups. Quartile 4 (Men: 5.67±0.35, Women: 4.70±0.32 Kg/m2) and quartile 1 (Men: 7.60±0.29, Women: 6.56±0.53 Kg/m2) represented low muscle mass and control groups, respectively. After multivariable adjustment, except for physical function, we found that blood urea nitrogen, creatinine, and age were associated with ASMI in men. However, only triglyceride level was related to ASMI in women. The multiple logistic regression models were used to analyze in each baseline characteristic and metabolite concentration. After the adjustment, we identify amino acid-related metabolites and show that glutamate levels in women and alpha-aminoadipate, Dopa, and citrulline/ornithine levels in men are gender-specific metabolic signatures of muscle mass loss.
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Affiliation(s)
- Chi-Jen Lo
- Metabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Taoyuan 333, Taiwan
| | - Yu-Shien Ko
- Division of Cardiology, Chang Gung Memorial Hospital, Taipei 105, Taiwan.,College of Medicine, Chang Gung University, Taoyuan 333, Taiwan
| | - Su-Wei Chang
- Clinical Informatics and Medical Statistics Research Center, Chang Gung University, Taoyuan 333, Taiwan
| | - Hsiang-Yu Tang
- Metabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Taoyuan 333, Taiwan
| | - Cheng-Yu Huang
- Metabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Taoyuan 333, Taiwan
| | - Yu-Chen Huang
- College of Medicine, Chang Gung University, Taoyuan 333, Taiwan.,Department of Thoracic Medicine, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Hung-Yao Ho
- Metabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Taoyuan 333, Taiwan.,Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan.,Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Chih-Ming Lin
- College of Medicine, Chang Gung University, Taoyuan 333, Taiwan.,Division of Internal Medicine, Chang Gung Memorial Hospital, Taipei 105, Taiwan.,Department of Health Management, Chang Gung Health and Culture Village, Taoyuan 333, Taiwan
| | - Mei-Ling Cheng
- Metabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Taoyuan 333, Taiwan.,Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan.,Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan
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46
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Rybtsova N, Berezina T, Kagansky A, Rybtsov S. Can Blood-Circulating Factors Unveil and Delay Your Biological Aging? Biomedicines 2020; 8:E615. [PMID: 33333870 PMCID: PMC7765271 DOI: 10.3390/biomedicines8120615] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/09/2020] [Accepted: 12/14/2020] [Indexed: 12/15/2022] Open
Abstract
According to the World Health Organization, the population of over 60 will double in the next 30 years in the developed countries, which will enforce a further raise of the retirement age and increase the burden on the healthcare system. Therefore, there is an acute issue of maintaining health and prolonging active working longevity, as well as implementation of early monitoring and prevention of premature aging and age-related disorders to avoid early disability. Traditional indicators of biological age are not always informative and often require extensive and expensive analysis. The study of blood factors is a simple and easily accessible way to assess individual health and supplement the traditional indicators of a person's biological age with new objective criteria. With age, the processes of growth and development, tissue regeneration and repair decline; they are gradually replaced by enhanced catabolism, inflammatory cell activity, and insulin resistance. The number of senescent cells supporting the inflammatory loop rises; cellular clearance by autophagy and mitophagy slows down, resulting in mitochondrial and cellular damage and dysfunction. Monitoring of circulated blood factors not only reflects these processes, but also allows suggesting medical intervention to prevent or decelerate the development of age-related diseases. We review the age-related blood factors discussed in recent publications, as well as approaches to slowing aging for healthy and active longevity.
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Affiliation(s)
- Natalia Rybtsova
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh EH16 4UU, UK;
| | - Tatiana Berezina
- Department of Scientific Basis of Extreme Psychology, Moscow State University of Psychology and Education, 127051 Moscow, Russia;
| | - Alexander Kagansky
- Centre for Genomic and Regenerative Medicine, School of Biomedicine, Far Eastern Federal University, 690922 Vladivostok, Russia
| | - Stanislav Rybtsov
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh EH16 4UU, UK;
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47
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Teruya T, Goga H, Yanagida M. Aging markers in human urine: A comprehensive, non-targeted LC-MS study. FASEB Bioadv 2020; 2:720-733. [PMID: 33336159 PMCID: PMC7734427 DOI: 10.1096/fba.2020-00047] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/04/2020] [Accepted: 09/28/2020] [Indexed: 12/25/2022] Open
Abstract
Metabolites in human biofluids document the physiological status of individuals. We conducted comprehensive, non-targeted, non-invasive metabolomic analysis of urine from 27 healthy human subjects, comprising 13 young adults (30 ± 3 years) and 14 seniors (76 ± 4 years). Quantitative analysis of 99 metabolites revealed 55 that displayed significant differences in abundance between the two groups. Forty-four did not show a statistically significant relationship with age. These include 13 standard amino acids, 5 methylated, 4 acetylated, and 9 other amino acids, 6 nucleosides, nucleobases, and derivatives, 4 sugar derivatives, 5 sugar phosphates, 4 carnitines, 2 hydroxybutyrates, 1 choline, and 1 ethanolamine derivative, and glutathione disulfide. Abundances of 53 compounds decreased, while 2 (glutathione disulfide, myo-inositol) increased in elderly people. The great majority of age-linked markers were highly correlated with creatinine. In contrast, 44 other urinary metabolites, including urate, carnitine, hippurate, and betaine, were not age-linked, neither declining nor increasing in elderly subjects. As metabolite profiles of urine and blood are quite different, age-related information in urine offers additional valuable insights into aging mechanisms of endocrine system. Correlation analysis of urinary metabolites revealed distinctly inter-related groups of compounds.
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Affiliation(s)
- Takayuki Teruya
- G0 Cell UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
| | - Haruhisa Goga
- G0 Cell UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
- Forensic Laboratory, Department of Criminal InvestigationOkinawa Prefectural Police HQOkinawaJapan
| | - Mitsuhiro Yanagida
- G0 Cell UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
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48
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Targeting metabolic pathways for extension of lifespan and healthspan across multiple species. Ageing Res Rev 2020; 64:101188. [PMID: 33031925 DOI: 10.1016/j.arr.2020.101188] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 08/20/2020] [Accepted: 09/21/2020] [Indexed: 12/16/2022]
Abstract
Metabolism plays a significant role in the regulation of aging at different levels, and metabolic reprogramming represents a major driving force in aging. Metabolic reprogramming leads to impaired organismal fitness, an age-dependent increase in susceptibility to diseases, decreased ability to mount a stress response, and increased frailty. The complexity of age-dependent metabolic reprogramming comes from the multitude of levels on which metabolic changes can be connected to aging and regulation of lifespan. This is further complicated by the different metabolic requirements of various tissues, cross-organ communication via metabolite secretion, and direct effects of metabolites on epigenetic state and redox regulation; however, not all of these changes are causative to aging. Studies in yeast, flies, worms, and mice have played a crucial role in identifying mechanistic links between observed changes in various metabolic traits and their effects on lifespan. Here, we review how changes in the organismal and organ-specific metabolome are associated with aging and how targeting of any one of over a hundred different targets in specific metabolic pathways can extend lifespan. An important corollary is that restriction or supplementation of different metabolites can change activity of these metabolic pathways in ways that improve healthspan and extend lifespan in different organisms. Due to the high levels of conservation of metabolism in general, translating findings from model systems to human beings will allow for the development of effective strategies for human health- and lifespan extension.
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49
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Dall'Olio L, Curti N, Remondini D, Safi Harb Y, Asselbergs FW, Castellani G, Uh HW. Prediction of vascular aging based on smartphone acquired PPG signals. Sci Rep 2020; 10:19756. [PMID: 33184391 PMCID: PMC7661535 DOI: 10.1038/s41598-020-76816-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 10/23/2020] [Indexed: 02/04/2023] Open
Abstract
Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) – the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking – was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.
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Affiliation(s)
- Lorenzo Dall'Olio
- Department of Physics and Astronomy, University of Bologna, 40126, Bologna, BO, Italy
| | - Nico Curti
- Department of Specialised, Diagnostic and Experimental Medicine, University of Bologna, 40126, Bologna, BO, Italy
| | - Daniel Remondini
- Department of Physics and Astronomy, University of Bologna, 40126, Bologna, BO, Italy
| | | | - Folkert W Asselbergs
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands.,Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, WC1E 6BT, UK.,Health Data Research UK and Institute of Health Informatics, University College London, London, NW1 2BE, UK
| | - Gastone Castellani
- Department of Specialised, Diagnostic and Experimental Medicine, University of Bologna, 40126, Bologna, BO, Italy
| | - Hae-Won Uh
- Department of Biostatistics and Research Support, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands.
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50
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Lozano-Gonzalez K, Padilla-Rodríguez E, Texis T, Gutiérrez MN, Rodríguez-Dorantes M, Cuevas-Córdoba B, Ramírez-García E, Mino-León D, Sánchez-García S, Gonzalez-Covarrubias V. Allele Frequency of ACE2 Intron Variants and Its Association with Blood Pressure. DNA Cell Biol 2020; 39:2095-2101. [PMID: 33016778 DOI: 10.1089/dna.2020.5804] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Angiotensin-converting enzyme 2 (ACE2) is known as the counter-regulator of the renin-angiotensin system, it cleaves angiotensin II to render Ag 1-7, a potent vasodilator with multiple roles in cardiovascular protection. A few studies have pinpointed ACE2 polymorphisms and their relationship with heart function and hypertension in a sex-dependent manner. These observations still lack replication mostly for admixed populations. This study aimed to report minor allele frequencies of four ACE2 intron variants, rs2285666, rs2048683, rs2106809, and rs4240157, derived from previous research using the GSA, v1.0, microarray in 1231 hypertensive and nonhypertensive patients. Logistic and multiple linear regression models were developed to identify potential associations with hypertension status and systolic and diastolic blood pressure (SBP and DBP). Allele frequency differences were identified for ACE2 rs2048683 and rs4240157 in populations with European ancestry and people of the Americas. Regression analyses identified a significant association of ACE2 rs2048683 and rs4240157 with SBP/DBP in males or females. Our observations confirm sex differences in ACE2 genetic associations with SBP and DBP and contribute to the collection of genetic variation in ACE2 for admixed populations.
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Affiliation(s)
| | | | - Tomas Texis
- Pharmacogenomics, Instituto Nacional de Medicina Genómica, INMEGEN, CDMX, México
| | - Marco N Gutiérrez
- Unidad de Investigación Epidemiológica y Servicios de Salud, Área envejecimiento, IMSS, CDMX, México
| | | | | | - Eliseo Ramírez-García
- Unidad de Investigación Epidemiológica y Servicios de Salud, Área envejecimiento, IMSS, CDMX, México
| | - Dolores Mino-León
- Unidad de Investigación en Epidemiología Clínica del Hospital de Especialidades del CMN Siglo XXI, IMSS, CDMX, México
| | - Sergio Sánchez-García
- Unidad de Investigación Epidemiológica y Servicios de Salud, Área envejecimiento, IMSS, CDMX, México
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