The DOI matches its Crossref record; 211 of the 215 references it cites were found in a registry or an index.
Metabolomics of Human Brain Aging and Age-Related Neurodegenerative Diseases
Abstract
Neurons in the mature human central nervous system (CNS) perform a wide range of motor, sensory, regulatory, behavioral, and cognitive functions. Such diverse functional output requires a great diversity of CNS neuronal and non-neuronal populations. Metabolomics encompasses the study of the complete set of metabolites/low-molecular-weight intermediates (metabolome), which are context-dependent and vary according to the physiology, developmental state, or pathologic state of the cell, tissue, organ, or organism. Therefore, the use of metabolomics can help to unravel the diversity-and to disclose the specificity-of metabolic traits and their alterations in the brain and in fluids such as cerebrospinal fluid and plasma, thus helping to uncover potential biomarkers of aging and neurodegenerative diseases. Here, we review the current applications of metabolomics in studies of CNS aging and certain age-related neurodegenerative diseases such as Alzheimer disease, Parkinson disease, and amyotrophic lateral sclerosis. Neurometabolomics will increase knowledge of the physiologic and pathologic functions of neural cells and will place the concept of selective neuronal vulnerability in a metabolic context.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Attempts to discover new biomarkers for the diagnosis of early stages of the disease are ongoing [134–136].”
From Brain-Derived Neurotrophic Factor in Brain Disorders: Focus on Neuroinflammation (2019) · cites this paper as [134]
Citations
Cites
1 retracted
Atkin, J. D., Farg, M. A., Walker, A. K., McLean, C., Tomas, D., & Horne, M. K. (2008) · Neurobiology of Disease · doi:10.1016/j.nbd.2008.02.009 · Cited by 350
RetractedFree radicals in biology and medicine
Gutteridge, J. M. C. (1991) · General Pharmacology The Vascular System · doi:10.1016/0306-3623(91)90478-o · Cited by 17,178
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
HMDB: the Human Metabolome Database
Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., Cheng, D., Jewell, K., Arndt, D., Sawhney, S., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl923 · Cited by 3,219
An anatomically comprehensive atlas of the adult human brain transcriptome
Hawrylycz, M. J., Lein, E. S., Guillozet-Bongaarts, A. L., Shen, E. H., Ng, L., Miller, J. A., van de Lagemaat, L. N., Smith, K. A., Ebbert, A., Riley, Z. L., et al. (2012) · Nature · doi:10.1038/nature11405 · Cited by 3,083
Ames, B. N., Cathcart, R., Schwiers, E., & Hochstein, P. (1981) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.78.11.6858 · Cited by 2,915
HMDB 3.0—The Human Metabolome Database in 2013
Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., Djoumbou-Feunang, Y., Mandal, R., Aziat, F., Dong, E., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1065 · Cited by 2,845
Metabolomics: the apogee of the omics trilogy
Patti, G. J., Yanes, Ó., & Siuzdak, G. E. (2012) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3314 · Cited by 2,605
The expensive-tissue hypothesis: The brain and the digestive system in human and primate evolution
Aiello, L. C., & Wheeler, P. (1995) · Current Anthropology · doi:10.1086/204350 · Cited by 2,291
Gene regulation and DNA damage in the ageing human brain
Lu, T., Pan, Y., Kao, S.-Y., Li, C., Kohane, I., Chan, J., & Yankner, B. A. (2004) · Nature · doi:10.1038/nature02661 · Cited by 1,874
Research works printed but not linked: 4
- Rasouri, 2007, [SIRT1/PGC-1: A neuroprotective axis?], Med Sci, 23, 840
- Carrié, 2000, Specific phospholipid fatty acid composition of brain regions in mice, Effects of n − 3 polyunsaturated fatty acid deficiency and phospholipid supplementation. J Lipid Res, 41, 465
- 10.1210/en.142.1.506 · DOI
- Lorenzo, 2006, Differential expression of GABAA and glycine receptors in ALS-resistant vs, ALS-vulnerable motoneurons: Possible implications for selective vulnerability of motoneurons. Eur J Neurosci, 23, 3161
Cited by
Sorted by citations
Metabolomics for Investigating Physiological and Pathophysiological Processes
Wishart, D. S. (2019) · Physiological Reviews · doi:10.1152/physrev.00035.2018 · Cited by 1,295
Brain Glucose Metabolism: Integration of Energetics with Function
Dienel, G. A. (2018) · Physiological Reviews · doi:10.1152/physrev.00062.2017 · Cited by 911
Brain-Derived Neurotrophic Factor in Brain Disorders: Focus on Neuroinflammation
Lima Giacobbo, B., Doorduin, J., Klein, H. C., Dierckx, R. A. J. O., Bromberg, E., & de Vries, E. F. J. (2019) · Molecular Neurobiology · doi:10.1007/s12035-018-1283-6 · Cited by 750
Ly, A., Buck, A., Balluff, B., Sun, N., Gorzolka, K., Feuchtinger, A., Janssen, K., Kuppen, P. J. K., van de Velde, C. J. �., Weirich, G., et al. (2016) · Nature Protocols · doi:10.1038/nprot.2016.081 · Cited by 243
Biomarkers for the Early Detection and Progression of Alzheimer's Disease
Counts, S. E., Ikonomovic, M. D., Mercado, N., Vega, I. E., & Mufson, E. J. (2017) · Neurotherapeutics · doi:10.1007/s13311-016-0481-z · Cited by 224
Lipidomics of Human Brain Aging and Alzheimer's Disease Pathology
Naudí, A., Cabré, R., Jové, M., Ayala, V., Gonzalo‐Benito, H., Portero‐Otín, M., Ferrer, I., & Pamplona, R. (2015) · International review of neurobiology · doi:10.1016/bs.irn.2015.05.008 · Cited by 214
Self-citationHDAC inhibitor–dependent transcriptome and memory reinstatement in cognitive decline models
Benito, E., Urbanke, H., Ramachandran, B., Barth, J., Halder, R., Awasthi, A., Jain, G., Capece, V., Burkhardt, S., Navarro-Sala, M., et al. (2015) · Journal of Clinical Investigation · doi:10.1172/jci79942 · Cited by 197
IMP dehydrogenase-2 drives aberrant nucleolar activity and promotes tumorigenesis in glioblastoma
Kofuji, S., Hirayama, A., Eberhardt, A. O., Kawaguchi, R. K., Sugiura, Y., Sampetrean, O., Ikeda, Y., Warren, M., Sakamoto, N., Kitahara, S., et al. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0363-9 · Cited by 171
Xu, J., Begley, P., Church, S. J., Patassini, S., Hollywood, K. A., Jüllig, M., Curtis, M. A., Waldvogel, H. J., Faull, R. L. M., Unwin, R. D., et al. (2016) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2016.03.001 · Cited by 174
Deregulation of purine metabolism in Alzheimer's disease
Ansoleaga, B., Jové, M., Schlüter, A., Garcia‐Esparcia, P., Moreno, J. R., Pujol, A., Pamplona, R., Portero‐Otín, M., & Ferrer, I. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.08.004 · Cited by 163
Self-citationMetabolomics in the study of retinal health and disease
Laíns, I., Gantner, M. L., Murinello, S., Lasky-Su, J. A., Miller, J. W., Friedlander, M., & Husain, D. (2018) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2018.11.002 · Cited by 152
Pharmacotherapeutic potential of ginger and its compounds in age-related neurological disorders
Choi, J. G., Kim, S. Y., Jeong, M., & Oh, M. S. (2017) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2017.08.010 · Cited by 155
Perakakis, N., Yazdani, A. Z. K., Karniadakis, G. E., & Mantzoros, C. S. (2018) · Metabolism · doi:10.1016/j.metabol.2018.08.002 · Cited by 155
Biomarker Research in Parkinson’s Disease Using Metabolite Profiling
Havelund, J. F., Heegaard, N. H. H., Færgeman, N. J., & Gramsbergen, J. B. (2017) · Metabolites · doi:10.3390/metabo7030042 · Cited by 147
Urinary Biomarkers of Brain Diseases
An, M., & Gao, Y. (2015) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2015.08.005 · Cited by 142
Metabolomic analysis in inflammatory bowel disease: A systematic review
Gallagher, K., Catesson, A., Griffin, J. L., Holmes, E., & Williams, H. R. T. (2021) · Journal of Crohn's and Colitis · doi:10.1093/ecco-jcc/jjaa227 · Cited by 133
UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry
Zhao, Y., Cheng, X., Vaziri, N. D., Liu, S., & Lin, R. (2014) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2014.07.019 · Cited by 130
Late-onset Alzheimer’s disease is associated with inherent changes in bioenergetics profiles
Sonntag, K.-C., Ryu, W.-I., Amirault, K. M., Healy, R. A., Siegel, A. J., McPhie, D. L., Forester, B., & Cohen, B. M. (2017) · Scientific Reports · doi:10.1038/s41598-017-14420-x · Cited by 131
Ryu, W., Bormann, M. K., Shen, M., Kim, D., Forester, B. P., Park, Y., So, J., Seo, H., Sonntag, K., & Cohen, B. M. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01068-3 · Cited by 126
Metabolomics and Lipidomics of Ischemic Stroke
Au, A. (2018) · Advances in clinical chemistry · doi:10.1016/bs.acc.2018.02.002 · Cited by 121
Emerging Insights into the Metabolic Alterations in Aging Using Metabolomics
Srivastava, S. (2019) · Metabolites · doi:10.3390/metabo9120301 · Cited by 124
Spatial and temporal diversity of glycome expression in mammalian brain
Lee, J., Ha, S., Kim, M., Kim, S.-W., Yun, J., Özcan, S., Hwang, H., Ji, I. J., Yin, D., Webster, M. J., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2014207117 · Cited by 109
Altered Metabolism in Alzheimer Disease Brain: Role of Oxidative Stress
Rummel, N. G., & Butterfield, D. A. (2021) · Antioxidants and Redox Signaling · doi:10.1089/ars.2021.0177 · Cited by 111
Functional MRI of brain physiology in aging and neurodegenerative diseases
Chen, J. J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.050 · Cited by 96
Metabolomics in the Development and Progression of Dementia: A Systematic Review
Jiang, Y., Zhu, Z., Shi, J., An, Y., Zhang, K., Wang, Y., Li, S., Li, J., Ye, W., Cui, M., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00343 · Cited by 88
Human Aging Is a Metabolome-related Matter of Gender
Jové, M., Maté, I., Naudí, A., Mota‐Martorell, N., Portero‐Otín, M., la Fuente, M. D., & Pamplona, R. (2015) · The Journals of Gerontology Series A · doi:10.1093/gerona/glv074 · Cited by 86
Self-citationA plasma metabolomic signature discloses human breast cancer
Jové, M., Collado, R., Quiles, J. L., Ramírez-Tortosa, M.-C., Sol, J., Ruíz-Sanjuan, M. D., Fernández, M., Cabrera, C. D. L. T., Ramírez-Tortosa, C. L., Granados-Principal, S. M., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.14521 · Cited by 93
Self-citationIn Pursuit of Healthy Aging: Effects of Nutrition on Brain Function
Melzer, T. M., Manosso, L. M., Yau, S., Gil‐Mohapel, J., & Brocardo, P. S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22095026 · Cited by 97
Jové, M., Pradas, I., Domínguez-González, M., Ferrer, I., & Pamplona, R. (2018) · Redox Biology · doi:10.1016/j.redox.2018.101082 · Cited by 84
Self-citationRashed, A. A., Rahman, A. Z. A., & Rathi, D.-N. G. (2021) · Molecules · doi:10.3390/molecules26041107 · Cited by 78
Tassone, F., Protic, D. D., Allen, E. G., Archibald, A. D., Baud, A., Brown, T. W., Budimirovic, D. B., Cohen, J., Dufour, B. D., Eiges, R., et al. (2023) · Cells · doi:10.3390/cells12182330 · Cited by 78
Zhou, H.-J., Zeng, C.-Y., Yang, T.-T., Long, F.-Y., Kuang, X., & Du, J.-R. (2018) · Life Sciences · doi:10.1016/j.lfs.2018.03.027 · Cited by 70
Meng, Q., Philip, M. C., Yang, N., & Sweedler, J. V. (2017) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.7b00304 · Cited by 70
Altered lipid metabolism in the aging kidney identified by three layered omic analysis
Braun, F., Rinschen, M. M., Bartels, V., Frommolt, P., Habermann, B. H., Hoeijmakers, J. H. J., Schumacher, B., Dollé, M. E. T., Müller, R., Benzing, T. R., et al. (2016) · Aging · doi:10.18632/aging.100900 · Cited by 73
Non-enzymatic molecular damage as a prototypic driver of aging
Golubev, A. G., Hanson, A. D., & Gladyshev, V. N. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.r116.751164 · Cited by 69
Plasma Nervonic Acid Is a Potential Biomarker for Major Depressive Disorder: A Pilot Study
Kageyama, Y., Kasahara, T., Nakamura, T., Hattori, K., Deguchi, Y., Tani, M., Kuroda, K., Yoshida, S., Goto, Y., Inoue, K., et al. (2017) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyx089 · Cited by 67
Zhou, W., Tong, D., Tian, D., Yu, Y., Huang, L., Zhang, W., Yu, Y., Lu, L., Zhang, X., Pan, W., et al. (2023) · Advanced Healthcare Materials · doi:10.1002/adhm.202301799 · Cited by 67
Structured Long-Chain Omega-3 Fatty Acids for Improvement of Cognitive Function during Aging
Mora, I., Arola, L., Caimari, A., Escoté, X., & Puiggròs, F. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23073472 · Cited by 74
Naudí, A., Cabré, R., Domínguez-González, M., Ayala, V., Jové, M., Mota‐Martorell, N., Piñol‐Ripoll, G., Gil‐Villar, M. P., Rué, M., Portero‐Otín, M., et al. (2017) · Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids · doi:10.1016/j.bbalip.2017.02.001 · Cited by 64
Self-citationHuman Plasma Metabolomics in Age-Related Macular Degeneration: Meta-Analysis of Two Cohorts
Laíns, I., Chung, W., Kelly, R. S., Gil, J., Marques, M., Barreto, P., Murta, J., Kim, I. K., Vavvas, D. G., Miller, J. B., et al. (2019) · Metabolites · doi:10.3390/metabo9070127 · Cited by 60
Kong, J., Xie, Y., Fan, R., Wang, Q., Luo, Y., & Dong, P. (2025) · European journal of medical research · doi:10.1186/s40001-025-02751-9 · Cited by 74
Metabolomic Analysis in Brain Research: Opportunities and Challenges
Vasilopoulou, C. G., Margarity, M., & Klapa, M. I. (2016) · Frontiers in Physiology · doi:10.3389/fphys.2016.00183 · Cited by 62
Yan, X., Zhao, X., Zhou, Z., McKay, A. S., Brunet, A., & Zare, R. N. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.0c02519 · Cited by 51
A Critical Evaluation of Wet Biomarkers for Huntington’s Disease: Current Status and Ways Forward
Silajdžić, E., & Björkqvist, M. (2018) · Journal of Huntington s Disease · doi:10.3233/jhd-170273 · Cited by 56
Xiao, Y., Dong, J., Yin, Z., Wu, Q., Zhou, Y., & Zhou, X. (2018) · Food and Chemical Toxicology · doi:10.1016/j.fct.2018.05.017 · Cited by 52
Metabolomics of Neurodegenerative Diseases
Botas, A., Campbell, H. M., Han, X., & Maletić‐Savatić, M. (2015) · International review of neurobiology · doi:10.1016/bs.irn.2015.05.006 · Cited by 52
Metabolomics as a Tool to Understand Pathophysiological Processes
Ivanišević, J., & Thomas, A. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7592-1_1 · Cited by 47
The Impact of Purinergic System Enzymes on Noncommunicable, Neurological, and Degenerative Diseases
Bagatini, M. D., dos Santos, A. A., Cardoso, A. M., Mânica, A., Reschke, C. R., & Carvalho, F. B. (2018) · Journal of Immunology Research · doi:10.1155/2018/4892473 · Cited by 50
Jasbi, P., Shi, X., Chu, P. S., Elliott, N. M., Hudson, H., Jones, D. C., Serrano, G. E., Chow, B., Beach, T. G., Liu, L., et al. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00290 · Cited by 46
Ruíz, M., Jové, M., Schlüter, A., Casasnovas, C., Villarroya, F., Guilera, C., Ortega, F. J., Naudí, A., Pamplona, R., Gimeno, R., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv375 · Cited by 46
Self-citationManiati, M. S., Maniati, M., Yousefi, T., Ahangar, A. A., & Tehrani, S. S. (2019) · Journal of Cellular Biochemistry · doi:10.1002/jcb.28361 · Cited by 45
Selective brain regional changes in lipid profile with human aging
Mota‐Martorell, N., Andrés‐Benito, P., Martin‐Garí, M., Galo‐Licona, J. D., Sol, J., Fernàndez‐Bernal, A., Portero‐Otín, M., Ferrer, I., Jové, M., & Pamplona, R. (2022) · GeroScience · doi:10.1007/s11357-022-00527-1 · Cited by 45
Self-citationA Potent Multi-functional Neuroprotective Derivative of Tetramethylpyrazine
Chen, H.-Y., Xu, D., Tan, G.-L., Cai, W., Zhang, G.-X., Cui, W., Wang, J.-Z., Long, C., Sun, Y.-W., Yu, P., et al. (2015) · Journal of Molecular Neuroscience · doi:10.1007/s12031-015-0566-x · Cited by 39
Sixty years old is the breakpoint of human frontal cortex aging
Cabré, R., Naudí, A., Domínguez-González, M., Ayala, V., Jové, M., Mota‐Martorell, N., Piñol‐Ripoll, G., Gil‐Villar, M. P., Rué, M., Portero‐Otín, M., et al. (2016) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2016.12.010 · Cited by 42
Self-citationOoi, K.-L. M., Vacy, K., & Boon, W. C. (2021) · Neurochemistry International · doi:10.1016/j.neuint.2021.105143 · Cited by 42
Guo, Z., Bao, D., Manor, B. D., & Zhou, J. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00275 · Cited by 40
Clark, C., Rabl, M., Dayon, L., & Popp, J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1065904 · Cited by 45
Tsai, K., Cheng, Y., Leu, H., Lee, Y., Chen, T., Liu, D.-H., & Kao, C.-L. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.05.012 · Cited by 40
Rodrigues, J. A., Narasimhamurthy, R. K., Joshi, M. B., Dsouza, H. S., & Mumbrekar, K. D. (2022) · Neurotoxicity Research · doi:10.1007/s12640-022-00534-2 · Cited by 41
CSF biomarkers in neurodegenerative and vascular dementias
Llorens, F., Schmitz, M., Ferrer, I., & Zerr, I. (2016) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2016.03.003 · Cited by 44
Self-citationPonnusamy, V., Kapellou, O., Yip, E., Evanson, J., Wong, L., Michael-Titus, A. T., Yip, P. K., & Shah, D. K. (2015) · Pediatric Research · doi:10.1038/pr.2015.276 · Cited by 38
Targeting miRNAs by polyphenols: Novel therapeutic strategy for aging
Majidinia, M., Karimian, A., Alemi, F., Yousefi, B., & Safa, A. (2019) · Biochemical Pharmacology · doi:10.1016/j.bcp.2019.113688 · Cited by 42
Finelli, M. J. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00254 · Cited by 38
Alzheimer's disease mechanisms in peripheral cells: Promises and challenges
Trushina, E. V. (2019) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1016/j.trci.2019.06.008 · Cited by 35
Kittimongkolsuk, P., Roxo, M., Li, H., Chuchawankul, S., Wink, M., & Tencomnao, T. (2021) · Pharmaceuticals · doi:10.3390/ph14020093 · Cited by 36
Sapkota, S., Huan, T., Tran, T., Zheng, J., Camicioli, R. M., Li, L., & Dixon, R. A. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00296 · Cited by 36
Jaeger, C., Glaab, E., Michelucci, A., Binz, T. M., Köglsberger, S., Garcia, P., Trezzi, J., Ghelfi, J., Balling, R., & Buttini, M. (2015) · American Journal Of Pathology · doi:10.1016/j.ajpath.2015.02.016 · Cited by 32
Three plasma metabolite signatures for diagnosing high altitude pulmonary edema
Guo, L., Tan, G., Liu, P., Li, H., Tang, L., Huang, L., & Ren, Q. (2015) · Scientific Reports · doi:10.1038/srep15126 · Cited by 32
Pickett, S. B., Thomas, E. D., Sebe, J. Y., Linbo, T. H., Esterberg, R., Hailey, D. W., & Raible, D. W. (2018) · eLife · doi:10.7554/elife.38062 · Cited by 32
Yang, Z.-H., Shi, C., Zhou, L., Li, Y., Yang, J., Liu, Y., Mao, C., Luo, H., Xu, G., & Xu, Y. (2019) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2019.00159 · Cited by 33
Jové, M., Mota‐Martorell, N., Torres, P., Ayala, V., Portero‐Otín, M., Ferrer, I., & Pamplona, R. (2021) · Life · doi:10.3390/life11050388 · Cited by 31
Self-citationSol, J., Òbis, È., Mota‐Martorell, N., Pradas, I., Galo‐Licona, J. D., Martin‐Garí, M., Fernàndez‐Bernal, A., Ortega, M., Mayneris‐Perxachs, J., Borrás, C., et al. (2023) · Aging Cell · doi:10.1111/acel.13821 · Cited by 31
Self-citationStatus of Metabolomic Measurement for Insights in Alzheimer’s Disease Progression—What Is Missing?
Yin, C., Harms, A. C., Hankemeier, T., Kindt, A. S. D., & de Lange, E. C. M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24054960 · Cited by 33
Arginine metabolism is a biomarker of red blood cell and human aging
Reisz, J. A., Earley, E. J., Nemkov, T. G., Key, A., Stephenson, D., Keele, G. R., Dzieciątkowska, M., Spitalnik, S. L., Hod, E. A., Kleinman, S. H., et al. (2024) · Aging Cell · doi:10.1111/acel.14388 · Cited by 33
Domínguez, M., de Oliveira, E., Odena, M. A., Portero‐Otín, M., Pamplona, R., & Ferrer, I. (2016) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2016.02.034 · Cited by 31
Self-citationChanges in Brain Metallome/Metabolome Pattern due to a Single i.v. Injection of Manganese in Rats
Neth, K., Lucio, M., Walker, A., Zorn, J., Schmitt‐Kopplin, P., & Michalke, B. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138270 · Cited by 30
Durani, L. W., Hamezah, H. S., Ibrahim, N. F., Yanagisawa, D., Nasaruddin, M. L., Mori, M., Azizan, K. A., Damanhuri, H. A., Makpol, S., Ngah, W. Z. W., et al. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-170880 · Cited by 31
Regional vulnerability to lipoxidative damage and inflammation in normal human brain aging
Domínguez-González, M., Puigpinós, M., Jové, M., Naudí, A., Portero‐Otín, M., Pamplona, R., & Ferrer, I. (2018) · Experimental Gerontology · doi:10.1016/j.exger.2018.07.023 · Cited by 29
Self-citationLiu, X., Zhou, Q., Zhu, X., Xie, L. P., & Cai, B. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.01261 · Cited by 29
Kittimongkolsuk, P., Pattarachotanant, N., Chuchawankul, S., Wink, M., & Tencomnao, T. (2021) · Biology · doi:10.3390/biology10010030 · Cited by 28
Plasma metabolomic profile and potential biomarkers for missed abortion
Fei, H., Hou, J., Wu, Z., Zhang, L., Zhao, H., Dong, X. W., & Chen, Y. (2016) · Biomedical Chromatography · doi:10.1002/bmc.3770 · Cited by 28
Bohnert, S., Reinert, C., Trella, S., Schmitz, W., Ondruschka, B., & Bohnert, M. (2020) · International Journal of Legal Medicine · doi:10.1007/s00414-020-02462-2 · Cited by 27
Serum metabolomic markers for traumatic brain injury: a mouse model
Bahado-Singh, R. O., Graham, S. F., Han, B., Türkoğlu, O., Ziadeh, J., Mandal, R., Er, A., Wishart, D. S., & Stahel, P. L. (2016) · Metabolomics · doi:10.1007/s11306-016-1044-3 · Cited by 27
Wu, J., Wuolikainen, A., Trupp, M., Jonsson, P., Marklund, S. L., Andersen, P. M., Forsgren, L., & Öhman, A. (2016) · Metabolomics · doi:10.1007/s11306-016-1041-6 · Cited by 27
Garcia‐Segura, M. E., Durainayagam, B. R., Liggi, S., Graça, G., Jiménez, B., Dehghan, A., Tzoulaki, I., Karaman, İ., Elliott, P., & Griffin, J. L. (2022) · Journal of Neurochemistry · doi:10.1111/jnc.15719 · Cited by 25
Jové, M., Gatius, S., Yeramian, A., Portero‐Otín, M., Eritja, N., Santacana, M., Colás, E., Ruiz, M., Pamplona, R., & Matías‐Guiu, X. (2016) · Oncotarget · doi:10.18632/oncotarget.10564 · Cited by 25
Self-citationBelete, G. T., Zhou, L., Li, K. K., So, P., Wai, C., & Lam, T. C. (2024) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2024.1403844 · Cited by 24
Serra, F. T., Cardoso, F. D. S., Petraconi, N., dos Santos, J. C. C., Araújo, B. H. S., Arida, R. M., & da Silva, S. G. (2021) · Neuroscience Letters · doi:10.1016/j.neulet.2021.136322 · Cited by 27
Shen, C., Sun, F., Zhang, R., Zhang, L., Li, Y.-L., Zhang, L., & Li, L. (2015) · Restorative Neurology and Neuroscience · doi:10.3233/rnn-150514 · Cited by 24
Metabolomics Predicts Neuroimaging Characteristics of Transient Ischemic Attack Patients
Purroy, F. F., Cambray, S., Mauri, G., Jové, M., Sanahuja, J., Farré, J., Benabdelhak, I., Molina‐Seguin, J., Campàs, L. C., Begue, R., et al. (2016) · EBioMedicine · doi:10.1016/j.ebiom.2016.11.010 · Cited by 24
Self-citationCabré, R., Naudí, A., Domínguez-González, M., Jové, M., Ayala, V., Mota‐Martorell, N., Pradas, I., Nogueras, L., Rué, M., Portero‐Otín, M., et al. (2017) · The Journals of Gerontology Series A · doi:10.1093/gerona/glx164 · Cited by 26
Self-citationCabré, R., Jové, M., Naudí, A., Ayala, V., Piñol‐Ripoll, G., Gil‐Villar, M. P., Domínguez-González, M., Òbis, È., Berdún, R., Mota‐Martorell, N., et al. (2016) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2016.00138 · Cited by 24
Self-citationCardiovascular Risk and Resilience Among Black Adults: Rationale and Design of the MECA Study
Islam, S. J., Kim, J. H., Topel, M., Liu, C., Ko, Y., Mujahid, M. S., Sims, M., Mubasher, M., Ejaz, K., Morgan‐Billingslea, J., et al. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.015247 · Cited by 23
Metabolic Sex Dimorphism of the Brain at the Gene, Cell, and Tissue Level
Lee, J. W., Profant, M. R., & Wang, C. (2022) · The Journal of Immunology · doi:10.4049/jimmunol.2100853 · Cited by 25
Pharmacotherapeutic potential of pomegranate in age-related neurological disorders
Kazemabad, M. J. E., Toni, S. A., Tizro, N., Dadkhah, P. A., Amani, H., Rezayat, S. A., Sheikh, Z., Mohammadi, M., Alijanzadeh, D., Alimohammadi, F., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.955735 · Cited by 25
The shift in the fatty acid composition of the circulating lipidome in Alzheimer's disease
Dakterzada, F., Jové, M., Cantero, J. L., Mota‐Martorell, N., Pamplona, R., & Piñoll‐Ripoll, G. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13792 · Cited by 23
Self-citationSharma, U., Upadhyay, D., Mewar, S., Mishra, A., Das, P., Gupta, S. D., Dwivedi, S. N., Makharia, G. K., & Jagannathan, N. R. (2015) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.12979 · Cited by 22
Dienel, G. A. (2018) · Neurochemical Research · doi:10.1007/s11064-018-2611-y · Cited by 22
Biomarkers of Neurodegeneration and Precision Therapy in Retinal Disease
Micera, A., Balzamino, B. O., Zazzo, A. D., Dinice, L., Bonini, S., & Coassin, M. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.601647 · Cited by 23
Lella, C., Nestor, L., Bundel, D. D., Heyden, Y. V., & Eeckhaut, A. V. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252212410 · Cited by 20
Metadata
- Authors
- Jové, M., Portero-Otín, M., Naudí, A., Ferrer, I., & Pamplona, R.
- Affiliations
- Universitat de Lleida (ES)
- Corresponding author
- Pamplona, Reinald
- Venue
- Journal of Neuropathology & Experimental Neurology
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-07
- Volume
- 73
- Issue
- 7
- Pages
- 640-657
- ISSN
- 0022-3069 (ISSN-L)
- 1554-6578
- OpenAlex
- W2317167530
- Cited by
- 195
- References
- 215
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 13 · 2023: 15 · 2022: 12 · 2021: 21 · 2020: 19 · 2019: 18 · 2018: 20 · 2017: 22 · 2016: 19 · 2015: 20 · 2014: 3
- FWCI
- 2.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neuropathology & Experimental Neurology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- brain aging, age-related neurodegenerative diseases, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, cerebrospinal fluid metabolites, plasma metabolites, aging biomarkers, selective neuronal vulnerability, central nervous system metabolism
- MeSH terms
- Aged, Aged, 80 and over, Aging, Alzheimer Disease, Amyotrophic Lateral Sclerosis, Brain, Energy Metabolism, Female, Humans, Male, Neurons, Parkinson Disease, Species Specificity, Biomarkers, Neurodegenerative Diseases, Metabolomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review