1
|
Fernández-Duval G, Razquin C, Wang F, Yun H, Hu J, Guasch-Ferré M, Rexrode K, Balasubramanian R, García-Gavilán J, Ruiz-Canela M, Clish CB, Corella D, Gómez-Gracia E, Fiol M, Estruch R, Lapetra J, Fitó M, Serra-Majem L, Ros E, Liang L, Dennis C, Asensio EM, Castañer O, Planes FJ, Salas-Salvadó J, Hu FB, Toledo E, Martínez-González MA. A multi-metabolite signature robustly predicts long-term mortality in the PREDIMED trial and several US cohorts. Metabolism 2025:156195. [PMID: 40107652 DOI: 10.1016/j.metabol.2025.156195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2024] [Revised: 03/07/2025] [Accepted: 03/15/2025] [Indexed: 03/22/2025]
Abstract
Metabolome-based biomarkers contribute to identify mechanisms of disease and to a better understanding of overall mortality. In a long-term follow-up subsample (n = 1878) of the PREDIMED trial, among 337 candidate baseline plasma metabolites repeatedly assessed at baseline and after 1 year, 38 plasma metabolites were identified as predictors of all-cause mortality. Gamma-amino-butyric acid (GABA), homoarginine, serine, creatine, 1-methylnicotinamide and a set of sphingomyelins, plasmalogens, phosphatidylethanolamines and cholesterol esters were inversely associated with all-cause mortality, whereas plasma dimethylguanidino valeric acid (DMGV), choline, short and long-chain acylcarnitines, 4-acetamidobutanoate, pseudouridine, 7-methylguanine, N6-acetyllysine, phenylacetylglutamine and creatinine were associated with higher mortality. The multi-metabolite signature created as a linear combination of these selected metabolites, also showed a strong association with all-cause mortality using plasma samples collected at 1-year follow-up in PREDIMED. This association was subsequently confirmed in 4 independent American cohorts, validating the signature as a consistent predictor of all-cause mortality across diverse populations.
Collapse
Affiliation(s)
- Gonzalo Fernández-Duval
- Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IdiSNA), University of Navarra, Pamplona, Spain; Institute of Data Science and Artificial Intelligence (DATAI), University of Navarra, Pamplona, Spain.
| | - Cristina Razquin
- Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IdiSNA), University of Navarra, Pamplona, Spain; Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Huan Yun
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jie Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Kathryn Rexrode
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Division of Women's Health, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Raji Balasubramanian
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Division of Women's Health, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jesús García-Gavilán
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Miguel Ruiz-Canela
- Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IdiSNA), University of Navarra, Pamplona, Spain; Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Clary B Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dolores Corella
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain
| | - Enrique Gómez-Gracia
- Department of Preventive Medicine, University of Malaga, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain
| | - Miquel Fiol
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Platform for Clinical Trials, Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Palma de Mallorca, Spain
| | - Ramón Estruch
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain
| | - José Lapetra
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Department of Family Medicine, Research Unity, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain
| | - Montse Fitó
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Hospital del Mar Research Institute, Barcelona, Spain
| | - Luis Serra-Majem
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Nutrition Research Group, Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Emilio Ros
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain
| | - 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
| | - Courtney Dennis
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eva M Asensio
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain
| | - Olga Castañer
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Hospital del Mar Research Institute, Barcelona, Spain
| | - Francisco J Planes
- Institute of Data Science and Artificial Intelligence (DATAI), University of Navarra, Pamplona, Spain; Tecnun School of Engineering, University of Navarra, San Sebastián, Spain; Biomedical Engineering Center, University of Navarra, Pamplona, Spain
| | - Jordi Salas-Salvadó
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Estefanía Toledo
- Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IdiSNA), University of Navarra, Pamplona, Spain; Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Miguel A Martínez-González
- Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IdiSNA), University of Navarra, Pamplona, Spain; Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
2
|
Lin CN, Hsu KC, Huang KL, Huang WC, Hung YL, Lee TH. Identification of Metabolomics Biomarkers in Extracranial Carotid Artery Stenosis. Cells 2022; 11:3022. [PMID: 36230983 PMCID: PMC9563778 DOI: 10.3390/cells11193022] [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: 07/16/2022] [Revised: 08/28/2022] [Accepted: 09/22/2022] [Indexed: 11/16/2022] Open
Abstract
The biochemical identification of carotid artery stenosis (CAS) is still a challenge. Hence, 349 male subjects (176 normal controls and 173 stroke patients with extracranial CAS ≥ 50% diameter stenosis) were recruited. Blood samples were collected 14 days after stroke onset with no acute illness. Carotid plaque score (≥2, ≥5 and ≥8) was used to define CAS severity. Serum metabolites were analyzed using a targeted Absolute IDQ®p180 kit. Results showed hypertension, diabetes, smoking, and alcohol consumption were more common, but levels of diastolic blood pressure, HDL-C, LDL-C, and cholesterol were lower in CAS patients than controls (p < 0.05), suggesting intensive medical treatment for CAS. PCA and PLS-DA did not demonstrate clear separation between controls and CAS patients. Decision tree and random forest showed that acylcarnitine species (C4, C14:1, C18), amino acids and biogenic amines (SDMA), and glycerophospholipids (PC aa C36:6, PC ae C34:3) contributed to the prediction of CAS. Metabolite panel analysis showed high specificity (0.923 ± 0.081, 0.906 ± 0.086 and 0.881 ± 0.109) but low sensitivity (0.230 ± 0.166, 0.240 ± 0.176 and 0.271 ± 0.169) in the detection of CAS (≥2, ≥5 and ≥8, respectively). The present study suggests that metabolomics profiles could help in differentiating between controls and CAS patients and in monitoring the progression of CAS.
Collapse
Affiliation(s)
- Chia-Ni Lin
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan 333, Taiwan
| | - Kai-Cheng Hsu
- School of Medicine, College of Medicine, Artificial Intelligence Center for Medical Diagnosis, and Department of Neurology, China Medical University Hospital, Taichung 404327, Taiwan
| | - Kuo-Lun Huang
- Stroke Center and Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
- College of Medicine, Chang Gung University, Taoyuan 333, Taiwan
| | - Wen-Cheng Huang
- Department of Nuclear Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Yi-Lun Hung
- Department of Nuclear Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Tsong-Hai Lee
- Stroke Center and Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
- College of Medicine, Chang Gung University, Taoyuan 333, Taiwan
| |
Collapse
|
3
|
Yun H, Sun L, Wu Q, Luo Y, Qi Q, Li H, Gu W, Wang J, Ning G, Zeng R, Zong G, Lin X. Lipidomic Signatures of Dairy Consumption and Associated Changes in Blood Pressure and Other Cardiovascular Risk Factors Among Chinese Adults. Hypertension 2022; 79:1617-1628. [PMID: 35469422 DOI: 10.1161/hypertensionaha.122.18981] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Omics data may provide a unique opportunity to discover dairy-related biomarkers and their linked cardiovascular health. METHODS Dairy-related lipidomic signatures were discovered in baseline data from a Chinese cohort study (n=2140) and replicated in another Chinese study (n=212). Dairy intake was estimated by a validated food-frequency questionnaire. Lipidomics was profiled by high-coverage liquid chromatography-tandem mass spectrometry. Associations of dairy-related lipids with 6-year changes in cardiovascular risk factors were examined in the discovery cohort, and their causalities were analyzed by 2-sample Mendelian randomization using available genome-wide summary data. RESULTS Of 350 lipid metabolites, 4 sphingomyelins, namely sphingomyelin (OH) C32:2, sphingomyelin C32:1, sphingomyelin (2OH) C30:2, and sphingomyelin (OH) C38:2, were identified and replicated to be positively associated with total dairy consumption (β=0.130 to 0.148; P<1.43×10-4), but not or weakly with nondairy food items. The score of 4 sphingomyelins showed inverse associations with 6-year changes in systolic (-2.68 [95% CI, -4.92 to -0.43]; P=0.019), diastolic blood pressures (-1.86 [95% CI, -3.12 to -0.61]; P=0.004), and fasting glucose (-0.25 [95% CI, -0.41 to -0.08]; P=0.003). Mendelian randomization analyses further revealed that genetically inferred sphingomyelin (OH) C32:2 was inversely associated with systolic (-0.57 [95% CI, -0.85 to -0.28]; P=9.16×10-5) and diastolic blood pressures (-0.39 [95% CI, -0.59 to -0.20]; P=7.09×10-5). CONCLUSIONS The beneficial effects of dairy products on cardiovascular health might be mediated through specific sphingomyelins among Chinese with overall low dairy consumption.
Collapse
Affiliation(s)
- Huan Yun
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Liang Sun
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Qingqing Wu
- CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, China (Q.W., R.Z.)
| | - Yaogan Luo
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY (Q.Q.)
| | - Huaixing Li
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Weiqiong Gu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.).,Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.)
| | - Jiqiu Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.).,Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.)
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.).,Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.)
| | - Rong Zeng
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study (R.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study (R.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, China (Q.W., R.Z.)
| | - Geng Zong
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xu Lin
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study (R.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study (R.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| |
Collapse
|
4
|
Metabolites Associated with Memory and Gait: A Systematic Review. Metabolites 2022; 12:metabo12040356. [PMID: 35448544 PMCID: PMC9024701 DOI: 10.3390/metabo12040356] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/08/2022] [Accepted: 04/13/2022] [Indexed: 01/19/2023] Open
Abstract
We recently found that dual decline in memory and gait speed was consistently associated with an increased risk of dementia compared to decline in memory or gait only or no decline across six aging cohorts. The mechanisms underlying this relationship are unknown. We hypothesize that individuals who experience dual decline may have specific pathophysiological pathways to dementia which can be indicated by specific metabolomic signatures. Here, we summarize blood-based metabolites that are associated with memory and gait from existing literature and discuss their relevant pathways. A total of 39 eligible studies were included in this systematic review. Metabolites that were associated with memory and gait belonged to five shared classes: sphingolipids, fatty acids, phosphatidylcholines, amino acids, and biogenic amines. The sphingolipid metabolism pathway was found to be enriched in both memory and gait impairments. Existing data may suggest that metabolites from sphingolipids and the sphingolipid metabolism pathway are important for both memory and gait impairments. Future studies using empirical data across multiple cohorts are warranted to identify metabolomic signatures of dual decline in memory and gait and to further understand its relationship with future dementia risk.
Collapse
|
5
|
Yun H, Qi QB, Zong G, Wu QQ, Niu ZH, Chen SS, Li HX, Sun L, Zeng R, Lin X. Plasma Sphingolipid Profile in Association with Incident Metabolic Syndrome in a Chinese Population-Based Cohort Study. Nutrients 2021; 13:nu13072263. [PMID: 34208976 PMCID: PMC8308381 DOI: 10.3390/nu13072263] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/23/2021] [Accepted: 06/26/2021] [Indexed: 11/17/2022] Open
Abstract
Although bioactive sphingolipids have been shown to regulate cardiometabolic homeostasis and inflammatory signaling pathways in rodents, population-based longitudinal studies of relationships between sphingolipids and onset of metabolic syndrome (MetS) are sparse. We aimed to determine associations of circulating sphingolipids with inflammatory markers, adipokines, and incidence of MetS. Among 1242 Chinese people aged 50–70 years who completed the 6-year resurvey, 76 baseline plasma sphingolipids were quantified by high-throughput liquid chromatography-tandem mass spectrometry. There were 431 incident MetS cases at 6-year revisit. After multivariable adjustment including lifestyle characteristics and BMI, 21 sphingolipids mainly from ceramide and hydroxysphingomyelin subclasses were significantly associated with incident MetS. Meanwhile, the baseline ceramide score was positively associated (RRQ4 versus Q1 = 1.31; 95% CI 1.05, 1.63; ptrend = 0.010) and the hydroxysphingomyelin score was inversely associated (RRQ4 versus Q1 = 0.60; 95% CI 0.45, 0.79; ptrend < 0.001) with incident MetS. When further controlling for clinical lipids, both associations were attenuated but remained significant. Comparing extreme quartiles, RRs (95% CIs) of MetS risk were 1.34 (95% CI 1.06, 1.70; ptrend = 0.010) for ceramide score and 0.71 (95% CI 0.51, 0.97; ptrend = 0.018) for hydroxysphingomyelin score, respectively. Furthermore, a stronger association between ceramide score and incidence of MetS was evidenced in those having higher inflammation levels (RRQ4 versus Q1 1.57; 95% CI 1.16, 2.12; pinteraction = 0.004). Our data suggested that elevated ceramide concentrations were associated with a higher MetS risk, whereas raised hydroxysphingomyelin levels were associated with a lower MetS risk beyond traditional clinical lipids.
Collapse
Affiliation(s)
- Huan Yun
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (H.Y.); (G.Z.); (Z.-H.N.); (S.-S.C.); (H.-X.L.); (L.S.)
| | - Qi-Bin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA;
| | - Geng Zong
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (H.Y.); (G.Z.); (Z.-H.N.); (S.-S.C.); (H.-X.L.); (L.S.)
| | - Qing-Qing Wu
- CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; (Q.-Q.W.); (R.Z.)
| | - Zhen-Hua Niu
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (H.Y.); (G.Z.); (Z.-H.N.); (S.-S.C.); (H.-X.L.); (L.S.)
| | - Shuang-Shuang Chen
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (H.Y.); (G.Z.); (Z.-H.N.); (S.-S.C.); (H.-X.L.); (L.S.)
| | - Huai-Xing Li
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (H.Y.); (G.Z.); (Z.-H.N.); (S.-S.C.); (H.-X.L.); (L.S.)
| | - Liang Sun
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (H.Y.); (G.Z.); (Z.-H.N.); (S.-S.C.); (H.-X.L.); (L.S.)
| | - Rong Zeng
- CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; (Q.-Q.W.); (R.Z.)
| | - Xu Lin
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (H.Y.); (G.Z.); (Z.-H.N.); (S.-S.C.); (H.-X.L.); (L.S.)
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
- Correspondence:
| |
Collapse
|
6
|
Human Brain Lipidomics: Utilities of Chloride Adducts in Flow Injection Analysis. Life (Basel) 2021; 11:life11050403. [PMID: 33924945 PMCID: PMC8145723 DOI: 10.3390/life11050403] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 04/25/2021] [Accepted: 04/26/2021] [Indexed: 11/16/2022] Open
Abstract
Ceramides have been implicated in a number of disease processes. However, current means of evaluation with flow infusion analysis (FIA) have been limited primarily due to poor sensitivity within our high-resolution mass spectrometry lipidomics analytical platform. To circumvent this deficiency, we investigated the potential of chloride adducts as an alternative method to improve sensitivity with electrospray ionization. Chloride adducts of ceramides and ceramide subfamilies provided 2- to 50-fold increases in sensitivity both with analytical standards and biological samples. Chloride adducts of a number of other lipids with reactive hydroxy groups were also enhanced. For example, monogalactosyl diacylglycerols (MGDGs), extracted from frontal lobe cortical gray and subcortical white matter of cognitively intact subjects, were not detected as ammonium adducts but were readily detected as chloride adducts. Hydroxy lipids demonstrate a high level of specificity in that phosphoglycerols and phosphoinositols do not form chloride adducts. In the case of choline glycerophospholipids, the fatty acid substituents of these lipids could be monitored by MS2 of the chloride adducts. Monitoring the chloride adducts of a number of key lipids offers enhanced sensitivity and specificity with FIA. In the case of glycerophosphocholines, the chloride adducts also allow determination of fatty acid substituents. The chloride adducts of lipids possessing electrophilic hydrogens of hydroxyl groups provide significant increases in sensitivity. In the case of glycerophosphocholines, chloride attachment to the quaternary ammonium group generates a dominant anion, which provides the identities of the fatty acid substituents under MS2 conditions.
Collapse
|
7
|
Wood PL, Muir W, Christmann U, Gibbons P, Hancock CL, Poole CM, Emery AL, Poovey JR, Hagg C, Scarborough JH, Christopher JS, Dixon AT, Craney DJ. Lipidomics of the chicken egg yolk: high-resolution mass spectrometric characterization of nutritional lipid families. Poult Sci 2021; 100:887-899. [PMID: 33518142 PMCID: PMC7858096 DOI: 10.1016/j.psj.2020.11.020] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 10/26/2020] [Accepted: 11/09/2020] [Indexed: 02/06/2023] Open
Abstract
While previous studies have characterized the fatty acids and global lipid families of the chicken egg yolk, there have been no publications characterizing the individual lipids in these lipid families. Such an in-depth characterization of egg yolk lipids is essential to define the potential benefits of egg yolk consumption for the supply of structural and anti-inflammatory lipids. Historically, the major focus has been on the cholesterol content of eggs and the potential negative health benefits of this lipid, while ignoring the essential roles of cholesterol in membranes and as a precursor to other essential sterols. A detailed analysis of egg yolk lipids, using high-resolution mass spectrometric analyses and tandem mass spectrometry to characterize the fatty acid substituents of complex structural lipids, was used to generate the first in-depth characterization of individual lipids within lipid families. Egg yolks were isolated from commercial eggs (Full Circle Market) and lipids extracted with methyl-t-butylether before analyses via high-resolution mass spectrometry. This analytical platform demonstrates that chicken egg yolks provide a rich nutritional source of complex structural lipids required for lipid homeostasis. These include dominant glycerophosphocholines (GPC) (34:2 and 36:2), plasmalogen GPC (34:1, 36:1), glycerophosphoethanolamines (GPE) 38:4 and 36:2), plasmalogen GPE (36:2 and 34:1), glycerophosphoserines (36:2 and 38:4), glycerophosphoinositols (38:4), glycerophosphoglycerols (36:2), N-acylphosphatidylethanolamines (NAPE) (56:6), plasmalogen NAPE (54:4 and 56:6), sphingomyelins (16:0), ceramides (22:0 and 24:0), cyclic phosphatidic acids (16:0 and 18:0), monoacylglycerols (18:1 and 18:2), diacylglycerols (36:3 and 36:2), and triacylglycerols (52:3). Our data indicate that the egg yolk is a rich source of structural and energy-rich lipids. In addition, the structural lipids possess ω-3 and ω-6 fatty acids that are essential precursors of endogenous anti-inflammatory lipid mediators. These data indicate that eggs are a valuable nutritional addition to the diets of individuals that do not have cholesterol issues.
Collapse
Affiliation(s)
- Paul L Wood
- College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA.
| | - William Muir
- College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| | - Undine Christmann
- College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| | - Philippa Gibbons
- College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| | - Courtney L Hancock
- College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| | - Cathleen M Poole
- College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| | - Audrey L Emery
- College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| | - Jesse R Poovey
- College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| | - Casey Hagg
- College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| | - Jon H Scarborough
- DeBusk College of Osteopathic Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| | - Jordon S Christopher
- DeBusk College of Osteopathic Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| | - Alexander T Dixon
- DeBusk College of Osteopathic Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| | - Dustin J Craney
- DeBusk College of Osteopathic Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA
| |
Collapse
|
8
|
Sphingolipids and physical function in the Atherosclerosis Risk in Communities (ARIC) study. Sci Rep 2021; 11:1169. [PMID: 33441925 PMCID: PMC7806657 DOI: 10.1038/s41598-020-80929-3] [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: 07/01/2020] [Accepted: 12/22/2020] [Indexed: 11/09/2022] Open
Abstract
Long-chain sphingomyelins (SMs) may play an important role in the stability of myelin sheath underlying physical function. The objective of this study was to examine the cross-sectional and longitudinal associations of long-chain SMs [SM (41:1), SM (41:2), SM (43:1)] and ceramides [Cer (41:1) and Cer (43:1)] with physical function in the Atherosclerosis Risk in Communities (ARIC) study. Plasma concentrations of SM (41:1), SM (41:2), SM (43:1), Cer (41:1) and Cer (43:1) were measured in 389 ARIC participants in 2011-13. Physical function was assessed by grip strength, Short Physical Performance Battery (SPPB), 4-m walking speed at both 2011-13 and 2016-17, and the modified Rosow-Breslau questionnaire in 2016-2017. Multivariable linear and logistic regression analyses were performed, controlling for demographic and clinical confounders. In cross-sectional analyses, plasma concentrations of SM 41:1 were positively associated with SPPB score (β-coefficients [95% confidence internal]: 0.33 [0.02, 0.63] per 1 standard deviation [SD] increase in log-transformed concentration, p value 0.04), 4-m walking speed (0.042 m/s [0.01, 0.07], p value 0.003), and negatively with self-reported disability (odds ratio = 0.73 [0.65, 0.82], p value < 0.0001). Plasma concentrations of the five metabolites examined were not significantly associated with longitudinal changes in physical function or incidence of poor mobility. In older adults, plasma concentrations of long-chain SM 41:1 were cross-sectionally positively associated with physical function.
Collapse
|
9
|
Nierenberg JL, He J, Li C, Gu X, Shi M, Razavi AC, Mi X, Li S, Bazzano LA, Anderson AH, He H, Chen W, Guralnik JM, Kinchen JM, Kelly TN. Serum metabolites associate with physical performance among middle-aged adults: Evidence from the Bogalusa Heart Study. Aging (Albany NY) 2020; 12:11914-11941. [PMID: 32482911 PMCID: PMC7343486 DOI: 10.18632/aging.103362] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 05/20/2020] [Indexed: 12/30/2022]
Abstract
Age-related declines in physical performance predict cognitive impairment, disability, chronic disease exacerbation, and mortality. We conducted a metabolome-wide association study of physical performance among Bogalusa Heart Study participants. Bonferroni corrected multivariate-adjusted linear regression was employed to examine cross-sectional associations between single metabolites and baseline gait speed (N=1,227) and grip strength (N=1,164). In a sub-sample of participants with repeated assessments of gait speed (N=282) and grip strength (N=201), significant metabolites from the cross-sectional analyses were tested for association with change in physical performance over 2.9 years of follow-up. Thirty-five and seven metabolites associated with baseline gait speed and grip strength respectively, including six metabolites that associated with both phenotypes. Three metabolites associated with preservation or improvement in gait speed over follow-up, including: sphingomyelin (40:2) (P=2.6×10-4) and behenoyl sphingomyelin (d18:1/22:0) and ergothioneine (both P<0.05). Seven metabolites associated with declines in gait speed, including: 1-carboxyethylphenylalanine (P=8.8×10-5), and N-acetylaspartate, N-formylmethionine, S-adenosylhomocysteine, N-acetylneuraminate, N2,N2-dimethylguanosine, and gamma-glutamylphenylalanine (all P<0.05). Two metabolite modules reflecting sphingolipid and bile acid metabolism associated with physical performance (minimum P=7.6×10-4). These results add to the accumulating evidence suggesting an important role of the human metabolome in physical performance and specifically implicate lipid, nucleotide, and amino acid metabolism in early physical performance decline.
Collapse
Affiliation(s)
- Jovia L Nierenberg
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA.,Department of Medicine, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA.,Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA 30606, USA
| | - Xiaoying Gu
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA.,Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, National Clinical Research Center of Respiratory Diseases, Beijing, China
| | - Mengyao Shi
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
| | - Alexander C Razavi
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
| | - Xuenan Mi
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
| | - Shengxu Li
- Children's Minnesota Research Institute, Children's Hospitals and Clinics of Minnesota, MN 55404, USA
| | - Lydia A Bazzano
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
| | - Amanda H Anderson
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
| | - Hua He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
| | - Wei Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
| | - Jack M Guralnik
- Division of Gerontology, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | | | - Tanika N Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
| |
Collapse
|
10
|
Li D, Misialek JR, Jack CR, Mielke MM, Knopman D, Gottesman R, Mosley T, Alonso A. Plasma Metabolites Associated with Brain MRI Measures of Neurodegeneration in Older Adults in the Atherosclerosis Risk in Communities⁻Neurocognitive Study (ARIC-NCS). Int J Mol Sci 2019; 20:ijms20071744. [PMID: 30970556 PMCID: PMC6479561 DOI: 10.3390/ijms20071744] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 03/28/2019] [Accepted: 04/01/2019] [Indexed: 12/30/2022] Open
Abstract
Background: Plasma metabolites are associated with cognitive and physical function in the elderly. Because cerebral small vessel disease (SVD) and neurodegeneration are common causes of cognitive and physical function decline, the primary objective of this study was to investigate the associations of six plasma metabolites (two plasma phosphatidylcholines [PCs]: PC aa C36:5 and PC aa 36:6 and four sphingomyelins [SMs]: SM C26:0, SM [OH] C22:1, SM [OH] C22:2, SM [OH] C24:1) with magnetic resonance imaging (MRI) features of cerebral SVD and neurodegeneration in older adults. Methods: This study included 238 older adults in the Atherosclerosis Risk in Communities study at the fifth exam. Multiple linear regression was used to assess the association of each metabolite (log-transformed) in separate models with MRI measures except lacunar infarcts, for which binary logistic regression was used. Results: Higher concentrations of plasma PC aa C36:5 had adverse associations with MRI features of cerebral SVD (odds ratio of 1.69 [95% confidence interval: 1.01, 2.83] with lacunar infarct, and beta of 0.16 log [cm3] [0.02, 0.30] with log [White Matter Hyperintensities (WMH) volume]) while higher concentrations of 3 plasma SM (OH)s were associated with higher total brain volume (beta of 12.0 cm3 [5.5, 18.6], 11.8 cm3 [5.0, 18.6], and 7.3 cm3 [1.2, 13.5] for SM [OH] C22:1, SM [OH] C22:2, and SM [OH] C24:1, respectively). Conclusions: This study identified associations between certain plasma metabolites and brain MRI measures of SVD and neurodegeneration in older adults, particularly higher SM (OH) concentrations with higher total brain volume.
Collapse
Affiliation(s)
- Danni Li
- Department of Lab Medicine and Pathology, University of Minnesota, 420 Delaware Street SE, MMC 609, Minneapolis, MN 55455, USA.
| | - Jeffrey R Misialek
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
| | - Michelle M Mielke
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55906, USA.
- Department of Neurology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
| | - David Knopman
- Department of Neurology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
| | - Rebecca Gottesman
- Department of Neurology, Johns Hopkins Medical Institutions, Baltimore, MD 21287, USA.
| | - Tom Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA.
| | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
| |
Collapse
|