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Association between fatty acid metabolism in the brain and Alzheimer disease neuropathology and cognitive performance: A nontargeted metabolomic study
Open accessAbstract
BACKGROUND: The metabolic basis of Alzheimer disease (AD) pathology and expression of AD symptoms is poorly understood. Omega-3 and -6 fatty acids have previously been linked to both protective and pathogenic effects in AD. However, to date little is known about how the abundance of these species is affected by differing levels of disease pathology in the brain. METHODS AND FINDINGS: We performed metabolic profiling on brain tissue samples from 43 individuals ranging in age from 57 to 95 y old who were stratified into three groups: AD (N = 14), controls (N = 14) and "asymptomatic Alzheimer's disease" (ASYMAD), i.e., individuals with significant AD neuropathology at death but without evidence for cognitive impairment during life (N = 15) from the autopsy sample of the Baltimore Longitudinal Study of Aging (BLSA). We measured 4,897 metabolite features in regions both vulnerable in the middle frontal and inferior temporal gyri (MFG and ITG) and resistant (cerebellum) to classical AD pathology. The levels of six unsaturated fatty acids (UFAs) in whole brain were compared in controls versus AD, and the differences were as follows: linoleic acid (p = 8.8 x 10-8, FC = 0.52, q = 1.03 x 10-6), linolenic acid (p = 2.5 x 10-4, FC = 0.84, q = 4.03 x 10-4), docosahexaenoic acid (p = 1.7 x 10-7, FC = 1.45, q = 1.24 x 10-6), eicosapentaenoic acid (p = 4.4 x 10-4, FC = 0.16, q = 6.48 x 10-4), oleic acid (p = 3.3 x 10-7, FC = 0.34, q = 1.46 x 10-6), and arachidonic acid (p = 2.98 x 10-5, FC = 0.75, q = 7.95 x 10-5). These fatty acids were strongly associated with AD when comparing the groups in the MFG and ITG, respectively: linoleic acid (p < 0.0001, p = 0.0006), linolenic acid (p < 0.0001, p = 0.002), docosahexaenoic acid (p < 0.0001, p = 0.0024), eicosapentaenoic acid (p = 0.0002, p = 0.0008), oleic acid (p < 0.0001, p = 0.0003), and arachidonic acid (p = 0.0001, p = 0.001). Significant associations were also observed between the abundance of these UFAs with neuritic plaque and neurofibrillary tangle burden as well as domain-specific cognitive performance assessed during life. Based on the regional pattern of differences in brain tissue levels of these metabolites, we propose that alterations in UFA metabolism represent both global metabolic perturbations in AD as well as those related to specific features of AD pathology. Within the middle frontal gyrus, decrements in linoleic acid, linolenic acid, and arachidonic acid (control>ASYMAD>AD) and increases in docosahexanoic acid (AD>ASYMAD>control) may represent regionally specific threshold levels of these metabolites beyond which the accumulation of AD pathology triggers the expression of clinical symptoms. The main limitation of this study is the relatively small sample size. There are few cohorts with extensive longitudinal cognitive assessments during life and detailed neuropathological assessments at death, such as the BLSA. CONCLUSIONS: The findings of this study suggest that unsaturated fatty acid metabolism is significantly dysregulated in the brains of patients with varying degrees of Alzheimer pathology.
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“It further corresponds with altered lipid metabolites and relevant gene expression in human AD. 53 , 54 , 55”
From Unique transcriptomic alterations in 5XFAD;PS19 mouse model identify glial lipid dysregulation and coordinated microglial–oligodendrocyte responses (2026) · cites this paper as [55]
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Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
Alzheimer's disease: the amyloid cascade hypothesis
Hardy, J. A., & Higgins, G. A. (1992) · Science · doi:10.1126/science.1566067 · Cited by 7,484
Prostaglandins and Leukotrienes: Advances in Eicosanoid Biology
Funk, C. D. (2001) · Science · doi:10.1126/science.294.5548.1871 · Cited by 3,817
Metabolomics — the link between genotypes and phenotypes
Fiehn, O. (2002) · Springer eBooks · doi:10.1007/978-94-010-0448-0_11 · Cited by 3,168
Epidemiology of Alzheimer disease
Reitz, C., Brayne, C., & Mayeux, R. (2011) · Nature Reviews Neurology · doi:10.1038/nrneurol.2011.2 · Cited by 1,675
Serhan, C. N., Hong, S., Gronert, K., Colgan, S. P., Devchand, P. R., Mirick, G., & Moussignac, R.-L. (2002) · The Journal of Experimental Medicine · doi:10.1084/jem.20020760 · Cited by 1,650
Omega-3 fatty acids EPA and DHA: Health benefits throughout life
Swanson, D., Block, R., & Mousa, S. A. (2012) · Advances in Nutrition · doi:10.3945/an.111.000893 · Cited by 1,575
Metabolomics--the link between genotypes and phenotypes.
Fiehn, O. (2002) · PubMed · Cited by 1,517
Cited by
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Lee, L. C., Liong, C.-Y., & Jemain, A. A. (2018) · The Analyst · doi:10.1039/c8an00599k · Cited by 823
Lipids and Alzheimer’s Disease
Kao, Y.-C., Ho, P.-C., Tu, Y.-K., Jou, I.-M., & Tsai, K.-J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21041505 · Cited by 485
Varma, V. R., Oommen, A. M., Varma, S., Casanova, R., An, Y., Andrews, R. M., O’Brien, R., Pletniková, O., Troncoso, J. C., Toledo, J. B., et al. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002482 · Cited by 469
Self-citationYin, F. (2023) · The FEBS Journal · doi:10.1111/febs.16344 · Cited by 441
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Chen, Y., Li, E., & Xu, L. (2022) · Metabolites · doi:10.3390/metabo12040357 · Cited by 391
Involvement of Lipids in Alzheimer’s Disease Pathology and Potential Therapies
Chew, H., Solomon, V. A., & Fonteh, A. N. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.00598 · Cited by 353
Solfrizzi, V., Custodero, C., Lozupone, M., Imbimbo, B. P., Valiani, V., Agosti, P., Schilardi, A., D’Introno, A., Montagna, M. L., Calvani, M., et al. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170248 · Cited by 358
The biological pathways of Alzheimer disease: a review
Calabrò, M., Rinaldi, C., Santoro, G., & Crisafulli, C. (2020) · AIMS neuroscience · doi:10.3934/neuroscience.2021005 · Cited by 287
Application of Metabolomics in Alzheimer’s Disease
Wilkins, J. M., & Trushina, E. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2017.00719 · Cited by 268
Cognitive impact of COVID-19: looking beyond the short term
Miners, J. S., Kehoe, P. G., & Love, S. (2020) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00744-w · Cited by 261
The Potential Role of Gut Microbiota in Alzheimer’s Disease: From Diagnosis to Treatment
Varesi, A., Pierella, E., Romeo, M., Piccini, G. B., Alfano, C., Bjørklund, G., Oppong, A., Ricevuti, G., Esposito, C., Chirumbolo, S., et al. (2022) · Nutrients · doi:10.3390/nu14030668 · Cited by 225
Chiu, H.-H., & Kuo, C. (2019) · Journal of Food and Drug Analysis · doi:10.1016/j.jfda.2019.10.003 · Cited by 242
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Muthubharathi, B. C., Gowripriya, T., & Balamurugan, K. (2021) · Molecular Omics · doi:10.1039/d0mo00176g · Cited by 226
Pan, L., Han, P., Ma, S., Peng, R., Wang, C., Kong, W., Cong, L., Fu, J., Zhang, Z., Yu, H., et al. (2019) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2019.10.007 · Cited by 204
Brain docosahexaenoic acid uptake and metabolism
Lacombe, R. J. S., Chouinard‐Watkins, R., & Bazinet, R. P. (2018) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.12.004 · Cited by 225
Axonal Degeneration during Aging and Its Functional Role in Neurodegenerative Disorders
Salvadores, N., Sanhueza, M., Manque, P., & Court, F. A. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00451 · Cited by 182
Horgusluoglu, E., Neff, R., Song, W., Wang, M., Wang, Q., Arnold, M., Krumsiek, J., Galindo‐Prieto, B., Ming, C., Nho, K., et al. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12468 · Cited by 175
Metabolism: A Novel Shared Link between Diabetes Mellitus and Alzheimer’s Disease
Sun, Y., Ma, C., Sun, H. J., Wang, H., Wei, P., Zhou, Z., Wang, H., Pi, C., Shi, Y., & He, X. (2020) · Journal of Diabetes Research · doi:10.1155/2020/4981814 · Cited by 181
Fatty acid metabolism in the progression and resolution of CNS disorders
Bogie, J. F. J., Haidar, M., Kooij, G., & Hendriks, J. J. A. (2020) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2020.01.004 · Cited by 167
Mahajan, U. V., Varma, V., Griswold, M., Blackshear, C., An, Y., Oommen, A., Varma, S., Troncoso, J., Pletnikova, O., O'Brien, R., et al. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003012 · Cited by 144
Self-citationMorris, G., Berk, M., Carvalho, A. F., Maes, M. H. J., Walker, A. J., & Puri, B. K. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.12.036 · Cited by 155
Gorji, N., Moeini, R., & Memariani, Z. (2017) · Pharmacological Research · doi:10.1016/j.phrs.2017.12.003 · Cited by 154
Brain and blood metabolome for Alzheimer's dementia: findings from a targeted metabolomics analysis
Huo, Z., Yu, L., Yang, J., Zhu, Y., Bennett, D. A., & Zhao, J. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.10.014 · Cited by 147
Omics sciences for systems biology in Alzheimer’s disease: State-of-the-art of the evidence
Hampel, H. J., Nisticò, R., Seyfried, N. T., Levey, A. I., Modeste, E. S., Lemercier, P., Baldacci, F., Toschi, N., Garaci, F., Perry, G. L., et al. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101346 · Cited by 147
Whiley, L., Chappell, K. E., D’Hondt, E., Lewis, M. R., Jiménez, B., Snowden, S. G., Soininen, H. S., Kłoszewska, I., Mecocci, P., Tsolaki, M., et al. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00741-z · Cited by 126
Self-citationTong, B., Ba, Y., Li, Z., Yang, C., Su, K., Qi, H., Zhang, D., Liu, X., Wu, Y., Chen, Y., et al. (2024) · Neurobiology of Disease · doi:10.1016/j.nbd.2024.106505 · Cited by 117
Sanguinetti, E., Collado, M. C., Marrachelli, V. G., Monleón, D., Selma‐Royo, M., Pardo-Tendero, M., Burchielli, S., & Iozzo, P. (2018) · Scientific Reports · doi:10.1038/s41598-018-23261-1 · Cited by 117
The Diverse Roles of microRNAs at the Host–Virus Interface
Bernier, A., & Sagan, S. M. (2018) · Viruses · doi:10.3390/v10080440 · Cited by 109
Absolute quantitative lipidomics reveals lipidome-wide alterations in aging brain
Tu, J., Yin, Y., Xu, M., Wang, R., & Zhu, Z. (2017) · Metabolomics · doi:10.1007/s11306-017-1304-x · Cited by 107
Lipids as biomarkers of brain disorders
Hussain, G., Anwar, H., Rasul, A., Imran, A., Qasim, M., Zafar, S., Imran, M., Kamran, S., Aziz, N., Razzaq, A., et al. (2019) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2018.1529653 · Cited by 105
A neurodegenerative cellular stress response linked to dark microglia and toxic lipid secretion
Flury, A. G., Aljayousi, L., Park, H.-J., Khakpour, M., Mechler, J., Aziz, S., McGrath, J. D., Deme, P., Sandberg, C., Ibáñez, F. G., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.11.018 · Cited by 102
Multiomics technologies: role in disease biomarker discoveries and therapeutics
Dar, M. A., Arafah, A. M., Bhat, K. A., Khan, A., Khan, M. S., Ali, A., Ahmad, S. M., Rashid, S. M., & Rehman, M. U. (2022) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac017 · Cited by 102
Extensive accumulation of misfolded protein aggregates during natural aging and senescence
Cuanalo-Contreras, K., Schulz, J., Mukherjee, A., Park, K.-W., Armijo, E., & Soto, C. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1090109 · Cited by 92
Shi, L., Baird, A. L., Westwood, S., Hye, A., Dobson, R., Thambisetty, M., & Lovestone, S. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170531 · Cited by 92
Self-citationNeurotransmitter Imbalance in the Brain and Alzheimer’s Disease Pathology
Snowden, S. G., Ebshiana, A. A., Hye, A., Pletniková, O., O’Brien, R., An, Y., Troncoso, J. C., Legido‐Quigley, C., & Thambisetty, M. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190577 · Cited by 89
Self-citationThe PERK-Dependent Molecular Mechanisms as a Novel Therapeutic Target for Neurodegenerative Diseases
Rozpędek-Kamińska, W., Siwecka, N., Wawrzynkiewicz, A., Wojtczak, R., Pytel, D., Diehl, J. A., & Majsterek, I. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21062108 · Cited by 84
Kim, M. G., Snowden, S. G., Suvitaival, T., Ali, A., Merkler, D. J., Ahmad, T., Westwood, S., Baird, A. L., Proitsi, P., Nevado‐Holgado, A. J., et al. (2019) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2019.03.004 · Cited by 88
Self-citationGu, X., Zhou, J., Zhou, Y., Wang, H., Si, N., Ren, W., Zhao, W., Fan, X., Gao, W., Wei, X., et al. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00779-7 · Cited by 81
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Huan, T., Tran, T., Zheng, J., Sapkota, S., MacDonald, S. W. S., Camicioli, R. M., Dixon, R. A., & Li, L. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-180711 · Cited by 81
Alteration of Metabolic Profile and Potential Biomarkers in the Plasma of Alzheimer’s Disease
Shao, Y., Ouyang, Y., Li, T., Liu, X., Xu, X., Li, S., Xu, G., & Le, W. (2020) · Aging and Disease · doi:10.14336/ad.2020.0217 · Cited by 79
Omar, S. H. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20112797 · Cited by 82
Čikić, S., Chandra, P. K., Harman, J. C., Rutkai, I., Katakam, P. V. G., Guidry, J. J., Gidday, J. M., & Busija, D. W. (2020) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x20915127 · Cited by 74
Integrative metabolomics science in Alzheimer’s disease: Relevance and future perspectives
Lista, S., González‐Domínguez, R., López‐Ortiz, S., González‐Domínguez, Á., Menéndez, H., Martín-Hernández, J., Lucía, A., Emanuele, E., Centonze, D., Imbimbo, B. P., et al. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.101987 · Cited by 76
Lipids in Pathophysiology and Development of the Membrane Lipid Therapy: New Bioactive Lipids
Torres, M., Parets, S., Fernández-Díaz, J., Beteta-Göbel, R., Rodríguez-Lorca, R., Román, R., Lladó, V., Rosselló, C. A., Fernández‐García, P., & Escribá, P. V. (2021) · Membranes · doi:10.3390/membranes11120919 · Cited by 80
Demarest, T. G., Varma, V. R., Estrada, D. J., Babbar, M., Basu, S., Mahajan, U. V., Moaddel, R., Croteau, D. L., Thambisetty, M., Mattson, M. P., et al. (2020) · Acta Neuropathologica · doi:10.1007/s00401-020-02152-8 · Cited by 74
Self-citationUntargeted lipidomics reveals progression of early Alzheimer’s disease in APP/PS1 transgenic mice
Zhang, X., Liu, W., Zan, J., Wu, C., & Tan, W. (2020) · Scientific Reports · doi:10.1038/s41598-020-71510-z · Cited by 69
Lipidomics of Bioactive Lipids in Alzheimer’s and Parkinson’s Diseases: Where Are We?
Chiurchiù, V., Tiberi, M., Matteocci, A., Fazio, F., Siffeti, H., Saracini, S., Mercuri, N. B., & Sancesario, G. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23116235 · Cited by 73
Roy, D., & Tedeschi, A. (2021) · Cells · doi:10.3390/cells10051078 · Cited by 70
Luo, Y., Yang, L.-L., & Yao, X. (2024) · Molecular Neurodegeneration · doi:10.1186/s13024-024-00720-0 · Cited by 75
Palavicini, J. P., Wang, C., Chen, L., HoSang, K. M., Wang, J., Tomiyama, T., Mori, H., & Han, X. (2017) · Acta Neuropathologica Communications · doi:10.1186/s40478-017-0460-6 · Cited by 62
Cao, Y., Zhao, L.-W., Chen, Z.-X., & Li, S.-H. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1430465 · Cited by 65
Hosseini, M., Poljak, A., Braidy, N., Crawford, J. D., & Sachdev, P. S. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101043 · Cited by 65
Innate sensing of mechanical properties of brain tissue by microglia
Ayata, P., & Schaefer, A. (2020) · Current Opinion in Immunology · doi:10.1016/j.coi.2020.01.003 · Cited by 57
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Dias, I. H. K., Milic, I., Heiß, C., Ademowo, O. S., Polidori, M. C., Devitt, A., & Griffiths, H. R. (2020) · Antioxidants and Redox Signaling · doi:10.1089/ars.2020.8022 · Cited by 57
Baldi, S., Menicatti, M., Nannini, G., Niccolai, E., Russo, E., Ricci, F., Pallecchi, M., Romano, F., Pedone, M., Poli, G., et al. (2021) · Nutrients · doi:10.3390/nu13030742 · Cited by 62
Liu, P., Yang, Q., Yu, N., Cao, Y., Wang, X., Wang, Z., Qiu, W.-Y., & Ma, C. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210461 · Cited by 59
Altered substrate metabolism in neurodegenerative disease: new insights from metabolic imaging
Cleland, N. R. W., Al‐Juboori, S. I., Dobrinskikh, E., & Bruce, K. D. (2021) · Journal of Neuroinflammation · doi:10.1186/s12974-021-02305-w · Cited by 62
Viana da Silva, S., Zhang, P., Haberl, M. G., Labrousse, V., Grosjean, N., Blanchet, C., Frick, A., & Mulle, C. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.2868-18.2019 · Cited by 58
Jia, T., Chu, C., Liu, Y., van Dongen, J., Papastergios, E., Armstrong, N. J., Bastin, M. E., Carrillo‐Roa, T., den Braber, A., Harris, M. A., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0605-z · Cited by 60
Caria, A. C. I., Nonaka, C. K. V., Cavalcante, B. R. R., Sousa, R. A. L. D., Aras, R., & Souza, B. S. D. F. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21144977 · Cited by 57
Liu, Y., Gu, X., Li, Y., Wang, F., Vyas, C. M., Peng, C., Dong, D., Li, Y., Zhang, Y., Zhang, Y., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03891-5 · Cited by 55
Nuclear Receptors as Therapeutic Targets for Neurodegenerative Diseases: Lost in Translation
Moutinho, M., Codocedo, J. F., Puntambekar, S. S., & Landreth, G. E. (2018) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010818-021807 · Cited by 57
Omics-based biomarkers discovery for Alzheimer's disease
Aerqin, Q., Wang, Z.-T., Wu, K., He, X., Dong, Q., & Yu, J. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04614-6 · Cited by 56
Sun, Y., Wei, K., Liao, X., Wang, J., Gao, L., & Pang, B. (2025) · International Journal of Molecular Medicine · doi:10.3892/ijmm.2025.5580 · Cited by 63
Interactions of polyunsaturated fatty acids with amyloid peptides Aβ40 and Aβ42
Shatshat, A. E., Pham, A. T., & Rao, P. P. N. (2018) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2018.12.027 · Cited by 53
High‐fat diet protects the blood–brain barrier in an Alzheimer's disease mouse model
Goldman, S. E., Goez, D., Last, D., Naor, S., Zaltsman, S. L., Sharvit‐Ginon, I., Atrakchi, D., Shemesh, C., Twitto‐Greenberg, R., Tsach, S., et al. (2018) · Aging Cell · doi:10.1111/acel.12818 · Cited by 52
Berman, P., Sulimani, L., Gelfand, A., Amsalem, K., Lewitus, G. M., & Meiri, D. (2020) · Talanta · doi:10.1016/j.talanta.2020.121336 · Cited by 48
Sánchez‐Alegría, K., & Arias, C. (2022) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.386 · Cited by 48
Relationship between Wine Consumption, Diet and Microbiome Modulation in Alzheimer’s Disease
Moreno-Arribas, M. V., Bartolomé, B., Peñalvo, J. L., Pérez‐Matute, P., & Motilva, M. (2020) · Nutrients · doi:10.3390/nu12103082 · 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
Mahapatra, G., Gao, Z., Bateman, J. R., Lockhart, S. N., Bergstrom, J., DeWitt, A. R., Piloso, J. E., Kramer, P. A., Gonzalez-Armenta, J. L., Amick, K. A., et al. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12731 · Cited by 47
Cerasuolo, M., Di Meo, I., Auriemma, M. C., Paolisso, G., Papa, M., & Rizzo, M. R. (2024) · Biomolecules · doi:10.3390/biom14111362 · Cited by 47
Pinçon, A., de Montgolfier, O., Akkoyunlu, N., Daneault, C., Pouliot, P., Villeneuve, L. R., Lesage, F., Levy, B. I., Thorin‐Trescases, N., Thorin, É., et al. (2019) · Metabolites · doi:10.3390/metabo9050104 · Cited by 47
Coverdale, J. P. C., Barnett, J. P., Adamu, A. H., Griffiths, E. J., Stewart, A. J., & Blindauer, C. A. (2019) · Metallomics · doi:10.1039/c9mt00177h · Cited by 45
Alzheimer’s Disease, a Lipid Story: Involvement of Peroxisome Proliferator-Activated Receptor α
Sáez-Orellana, F., Octave, J., & Pierrot, N. (2020) · Cells · doi:10.3390/cells9051215 · Cited by 47
Phospholipases in neuronal function: A role in learning and memory?
Joensuu, M., Wallis, T. P., Saber, S. H., & Meunier, F. A. (2019) · Journal of Neurochemistry · doi:10.1111/jnc.14918 · Cited by 47
A metabolite panel that differentiates Alzheimer's disease from other dementia types
Jia, L., Yang, J., Zhu, M., Pang, Y., Wang, Q., Wei, Q., Li, Y., Li, T., Li, F., Wang, Q., et al. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12484 · Cited by 46
DDHD2 provides a flux of saturated fatty acids for neuronal energy and function
Saber, S. H., Yak, N., Yong, X. L. H., Bong, Y. T., Leeson, H., Dai, C.-Y., Binder, T., Lu, S., Purushothaman, R., Lenaerts, A.-S., et al. (2025) · Nature Metabolism · doi:10.1038/s42255-025-01367-x · Cited by 44
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Agostini, A., Ding, Y., Bai, L., Kendall, D. A., & Pardon, M. (2019) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2019.09.019 · Cited by 43
Ooi, K.-L. M., Vacy, K., & Boon, W. C. (2021) · Neurochemistry International · doi:10.1016/j.neuint.2021.105143 · Cited by 42
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Sánchez‐Alegría, K., Bastián‐Eugenio, C. E., Vaca, L. A., & Arias, C. (2021) · The FASEB Journal · doi:10.1096/fj.202100243r · Cited by 40
González‐Ruiz, V., Pezzatti, J., Roux, A., Stoppini, L., Boccard, J., & Rudaz, S. L. (2017) · Journal of Chromatography A · doi:10.1016/j.chroma.2017.10.055 · Cited by 39
Gross, T. J., Doran, E. W., Cheema, A. K., Head, E., Lott, I. T., & Mapstone, M. E. (2019) · Developmental Neurobiology · doi:10.1002/dneu.22716 · Cited by 42
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Metadata
- Authors
- Snowden, S. G., Ebshiana, A. A., Hye, A., An, Y., Pletnikova, O., O’Brien, R., Troncoso, J., Legido-Quigley, C., & Thambisetty, M.
- Affiliations
- King's College London (GB)
- National Institute on Aging (US)
- Duke University (US)
- Johns Hopkins University (US)
- Corresponding author
- Thambisetty, Madhav
- Venue
- PLOS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-03-21
- Volume
- 14
- Issue
- 3
- Pages
- e1002266
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- PMC
- PMC5360226
- OpenAlex
- W2600991905
- Cited by
- 309
- References
- 68
- Citations by year
- 2026: 21 · 2025: 39 · 2024: 33 · 2023: 32 · 2022: 30 · 2021: 42 · 2020: 42 · 2019: 35 · 2018: 22 · 2017: 12
- FWCI
- 22.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- creativecommons.org/publicdomain/zero/1.0/ (vor)
- Locations
- PLoS Medicine (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Research Portal (King's College London) (submittedVersion, public-domain)
- Funders
- Innovative Medicines Initiative. (European Medical Information Framework grant agreement no. 115372)
- Innovative Medicines Initiative (European Medical Information Framework grant agreement no. 115372)
- libyan cultural attache (7922)
- Intramural Research Program of the NIH, National Institute on Aging
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Nursing › Nutrition and Dietetics › Fatty Acid Research and Health
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, brain fatty acid metabolism, unsaturated fatty acids, omega-3 fatty acids, omega-6 fatty acids, docosahexaenoic acid, eicosapentaenoic acid, linoleic acid, arachidonic acid, linolenic acid, oleic acid, Alzheimer's disease pathology, cognitive performance, middle frontal gyrus, inferior temporal gyrus, cerebellum
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Brain, Cognition, Fatty Acids, Female, Humans, Longitudinal Studies, Male, Middle Aged, Baltimore, Metabolome, Cognitive Dysfunction
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