Urinary metabolic phenotyping for Alzheimer’s disease
Open accessAbstract
Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer's Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer's Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer's Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer's Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer's pathology in previous studies.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The largest metabolic analysis on urine of AD and MCI patients has permitted to shape a urinary metabolic phenotyping.
“The largest metabolic analysis performed on urine of AD and MCI patients have permitted to shape a urinary metabolic phenotyping.”
From Role of Oxidative Damage in Alzheimer’s Disease and Neurodegeneration: From Pathogenic Mechanisms to Biomarker Discovery (2021) · cites this paper as [138]
Urine contains metabolites that reflect a response to injury, including OS, or carry information originated from the gut microbiome.
“Urine contains metabolites that reflect a response to injury, including OS, occurring at high distance or carry information originated from the gut microbiome, a novel area of research in neurodegeneration and AD [138].”
From Role of Oxidative Damage in Alzheimer’s Disease and Neurodegeneration: From Pathogenic Mechanisms to Biomarker Discovery (2021) · cites this paper as [138]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089 · see citing works
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879 · see citing works
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106 · see citing works
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,185 · see citing works
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 6,874 · see citing works
Ho, T. K. (2002) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.1995.598994 · Cited by 6,311 · see citing works
Host-gut microbiota metabolic interactions
Nicholson, J. K., Holmes, E., Kinross, J., Burcelin, R., Gibson, G., Jia, W., & Pettersson, S. (2012) · Science · doi:10.1126/science.1223813 · Cited by 4,760 · see citing works
Self-citationBuniello, A., MacArthur, J. A. L., Cerezo, M., Harris, L. W., Hayhurst, J., Malangone, C., McMahon, A., Morales, J., Mountjoy, E., Sollis, E., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1120 · Cited by 4,000 · see citing works
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 · see citing works
Self-citationGasque, G., Conway, S., Huang, J., Rao, Y., & Vosshall, L. B. (2013) · Scientific Reports · doi:10.1038/srep02120 · Cited by 2,320 · see citing works
METLIN—a metabolite mass spectral database
Smith, C. A., Maille, G. O., Want, E. J., Qin, C., Trauger, S. A., Brandon, T. R., Custodio, D. E., Abagyan, R., & Siuzdak, G. (2005) · Therapeutic Drug Monitoring · doi:10.1097/01.ftd.0000179845.53213.39 · Cited by 2,226 · see citing works
Gut microbiome alterations in Alzheimer’s disease
Vogt, N. M., Kerby, R. L., Dill-McFarland, K. A., Harding, S. J., Merluzzi, A. P., Johnson, S. C., Carlsson, C. M., Asthana, S., Zetterberg, H., Blennow, K., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13601-y · Cited by 2,137 · see citing works
Olsson, B., Lautner, R., Andreasson, U., Öhrfelt, A., Portelius, E., Bjerke, M., Hölttä, M., Rosén, C., Olsson, C., Strobel, G., et al. (2016) · The Lancet Neurology · doi:10.1016/s1474-4422(16)00070-3 · Cited by 2,049 · see citing works
Matrix eQTL: ultra fast eQTL analysis via large matrix operations
Shabalin, A. A. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts163 · Cited by 1,694 · see citing works
Plasma phospholipids identify antecedent memory impairment in older adults
Mapstone, M. E., Cheema, A. K., Fiandaca, M. S., Zhong, X., Mhyre, T. R., MacArthur, L. H., Hall, W. J., Fisher, S. G., Peterson, D. R., Haley, J. M., et al. (2014) · Nature Medicine · doi:10.1038/nm.3466 · Cited by 1,031 · see citing works
Genotype Imputation with Thousands of Genomes
Howie, B., Marchini, J., & Stephens, M. (2011) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.111.001198 · Cited by 966 · see citing works
MahmoudianDehkordi, S., Arnold, M., Nho, K., Ahmad, S., Jia, W., Xie, G., Louie, G., Kueider‐Paisley, A., Moseley, M. A., Thompson, J. W., et al. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.07.217 · Cited by 706 · see citing works
Dona, A. C., Jiménez, B., Schäfer, H., Humpfer, E., Spraul, M., Lewis, M. R., Pearce, J. T. M., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2014) · Analytical Chemistry · doi:10.1021/ac5025039 · Cited by 540 · see citing works
Self-citationProteome-based plasma biomarkers for Alzheimer's disease
Hye, A., Lynham, S., Thambisetty, M., Čaušević, M., Campbell, J., Byers, H. L., Hooper, C., Rijsdijk, F. V., Tabrizi, S. J., Banner, S. J., et al. (2006) · Brain · doi:10.1093/brain/awl279 · Cited by 463 · see citing works
Self-citationEvidence for brain glucose dysregulation in Alzheimer’s disease
An, Y., Varma, V., Varma, S., Casanova, R., Dammer, E., Pletnikova, O., Chia, C. W., Egan, J. M., Ferrucci, L., Troncoso, J., et al. (2017) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2017.09.011 · Cited by 454 · see citing works
Self-citationMatrix eQTL: Ultra fast eQTL analysis via large matrix operationsPreprint
Shabalin, A. A. (2011) · arXiv (Cornell University) · doi:10.17615/g28n-r219 · Cited by 452 · see citing works
Han, X., Rozen, S., Boyle, S. H., Hellegers, C., Cheng, H., Burke, J. R., Welsh-Bohmer, K. A., Doraiswamy, P. M., & Kaddurah-Daouk, R. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021643 · Cited by 429 · see citing works
Challenges, progress and promises of metabolite annotation for LC–MS-based metabolomics
Chaleckis, R., Meister, I., Zhang, P., & Wheelock, C. E. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.07.010 · Cited by 373 · see citing works
O’Bryant, S. E., Mielke, M. M., Rissman, R. A., Lista, S., Vanderstichele, H., Zetterberg, H., Lewczuk, P., Posner, H. B., Hall, J. R., Johnson, L. A., et al. (2016) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.09.014 · Cited by 304 · see citing works
A genome-wide association study of metabolic traits in human urine
Suhre, K., Wallaschofski, H., Raffler, J., Friedrich, N., Haring, R., Michael, K., Wasner, C., Krebs, A., Kronenberg, F., Chang, D., et al. (2011) · Nature Genetics · doi:10.1038/ng.837 · Cited by 246 · see citing works
Gut Microbiota and Their Neuroinflammatory Implications in Alzheimer’s Disease
Giau, V. V., Wu, S., Jamerlan, A., An, S. S. A., Kim, S., & Hulme, J. (2018) · Nutrients · doi:10.3390/nu10111765 · Cited by 235 · see citing works
Smith, C. M., Jerkovic, A., Truong, T. T., Foote, S. J., McCarthy, J. S., & McMorran, B. J. (2017) · Scientific Reports · doi:10.1038/srep41975 · Cited by 219 · see citing works
AddNeuroMed—The European Collaboration for the Discovery of Novel Biomarkers for Alzheimer's Disease
Lovestone, S., Francis, P. T., Kłoszewska, I., Mecocci, P., Simmons, A., Soininen, H. S., Spenger, C., Tsolaki, M., Vellas, B. J., Wahlund, L., et al. (2009) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2009.05064.x · Cited by 211 · see citing works
Self-citationPaterson, A. D., Lopes-Virella, M. F., Waggott, D. M., Boright, A. P., Hosseini, S. M., Carter, R. E., Enqing, S., Mirea, L., Bharaj, B., Sun, L., et al. (2009) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.109.192971 · Cited by 206 · see citing works
Association of blood lipids with Alzheimer's disease: A comprehensive lipidomics analysis
Proitsi, P., Kim, M. G., Whiley, L., Simmons, A., Sattlecker, M., Velayudhan, L., Lupton, M. K., Soininen, H., Kłoszewska, I., Mecocci, P., et al. (2016) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.08.003 · Cited by 203 · see citing works
Self-citationMitochondrial iron metabolism and its role in neurodegeneration
Horowitz, M. P., & Greenamyre, J. T. (2010) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-100354 · Cited by 195 · see citing works
Candini, O., Grisendi, G., Foppiani, E. M., Brogli, M., Aramini, B., Masciale, V., Spano, C., Petrachi, T., Veronesi, E., Conte, P., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-43613-9 · Cited by 194 · see citing works
The pre-synaptic vesicle protein synaptotagmin is a novel biomarker for Alzheimer’s disease
Öhrfelt, A., Brinkmalm, A., Dumurgier, J., Brinkmalm, G., Hansson, O., Zetterberg, H., Bouaziz-Amar, E., Hugon, J., Paquet, C., & Blennow, K. (2016) · Alzheimer's Research & Therapy · doi:10.1186/s13195-016-0208-8 · Cited by 184 · see citing works
Ashton, N. J., Leuzy, A., Lim, Y. M., Troakes, C., Hortobágyi, T., Höglund, K., Aarsland, D., Lovestone, S., Schöll, M., Blennow, K., et al. (2019) · Acta Neuropathologica Communications · doi:10.1186/s40478-018-0649-3 · Cited by 172 · see citing works
Self-citationCasanova, R., Varma, S., Simpson, B. N., Kim, M. G., An, Y., Saldana, S. J., Riveros, C., Moscato, P., Griswold, M. E., Sonntag, D., et al. (2016) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2015.12.008 · Cited by 162 · see citing works
Self-citationMarksteiner, J., Blasko, I., Kemmler, G., Koal, T., & Humpel, C. (2017) · Metabolomics · doi:10.1007/s11306-017-1297-5 · Cited by 158 · see citing works
Castillo, E., Leon, J., Mazzei, G., Abolhassani, N., Haruyama, N., Saito, T., Saido, T., Hokama, M., Iwaki, T., Ohara, T., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-17999-3 · Cited by 158 · see citing works
Cognitive impairment in primary biliary cirrhosis: Symptom impact and potential etiology†
Newton, J. L., Hollingsworth, K. G., Taylor, R., El‐Sharkawy, A. M., Khan, Z. U., Pearce, R., Sutcliffe, K., Okonkwo, O., Davidson, A., Burt, J., et al. (2008) · Hepatology · doi:10.1002/hep.22371 · Cited by 156 · see citing works
Metabolomic Profiling of Bile Acids in Clinical and Experimental Samples of Alzheimer’s Disease
Pan, X., Elliott, C. T., McGuinness, B., Passmore, P., Kehoe, P. G., Hölscher, C., McClean, P. L., Graham, S. F., & Green, B. D. (2017) · Metabolites · doi:10.3390/metabo7020028 · Cited by 153 · see citing works
Carvalho, C., Cardoso, S., Correia, S. C., Santos, R. X., Santos, M. S., Baldeiras, I., Oliveira, C. R., & Moreira, P. I. (2012) · Diabetes · doi:10.2337/db11-1186 · Cited by 152 · see citing works
Metabolic profiling of Alzheimer's disease brains
Inoue, K., Tsutsui, H., Akatsu, H., Hashizume, Y., Matsukawa, N., Yamamoto, T., & Toyo’oka, T. (2013) · Scientific Reports · doi:10.1038/srep02364 · Cited by 150 · see citing works
Metabolomics Data Normalization with EigenMS
Karpievitch, Y. V., Nikolic, S. B., Wilson, R., Sharman, J. E., & Edwards, L. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0116221 · Cited by 144 · see citing works
Lewis, M. R., Pearce, J. T. M., Spagou, K., Green, M., Dona, A. C., Yuen, A. H. Y., David, M., Berry, D. J., Chappell, K. E., der Sluis, V. H.-V., et al. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b01481 · Cited by 143 · see citing works
Self-citationUrinary Biomarkers of Brain Diseases
An, M., & Gao, Y. (2015) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2015.08.005 · Cited by 142 · see citing works
Hampel, H. J., Lista, S., Teipel, S. J., Garaci, F., Nisticò, R., Blennow, K., Zetterberg, H., Bertram, L., Duyckaerts, C. J., Bakardjian, H., et al. (2013) · Biochemical Pharmacology · doi:10.1016/j.bcp.2013.11.009 · Cited by 141 · see citing works
Nicholson, G., Rantalainen, M., Li, J. V., Maher, A. D., Malmodin, D., Ahmadi, K. R., Faber, J. H., Barrett, A., Min, J. L., Rayner, N. W., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002270 · Cited by 136 · see citing works
Self-citationMRI Measures of Alzheimer's Disease and the AddNeuroMed Study
Simmons, A., Westman, E., Muehlboeck, S., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Wahlund, L., Soininen, H. S., Lovestone, S., et al. (2009) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2009.05063.x · Cited by 128 · see citing works
Self-citationHua, X., Lei, M., Zhang, Y., Ding, J., Qun-ying, H., Hu, G., & Xiao, M. (2007) · Life Sciences · doi:10.1016/j.lfs.2007.02.030 · Cited by 125 · see citing works
Campion, D., Charbonnier, C., & Nicolas, G. (2019) · Acta Neuropathologica · doi:10.1007/s00401-019-01991-4 · Cited by 124 · see citing works
Plasma 24-metabolite Panel Predicts Preclinical Transition to Clinical Stages of Alzheimer’s Disease
Fiandaca, M. S., Zhong, X., Cheema, A. K., Orquiza, M. H., Chidambaram, S., Tan, M. T., Gresenz, C. R., Fitzgerald, K. T., Nalls, M. A., Singleton, A., et al. (2015) · Frontiers in Neurology · doi:10.3389/fneur.2015.00237 · Cited by 109 · see citing works
Blood‐based metabolic signatures in Alzheimer's disease
de Leeuw, F. A., Peeters, C. F. W., Kester, M. I., Harms, A. C., Struys, E. A., Hankemeier, T., van Vlijmen, H. W. T., van der Lee, S. J., van Duijn, C. M., Scheltens, P., et al. (2017) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2017.07.006 · Cited by 102 · see citing works
Raffler, J., Friedrich, N., Arnold, M., Kacprowski, T., Rueedi, R., Altmaier, E., Bergmann, S., Budde, K., Gieger, C., Homuth, G., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005487 · Cited by 101 · see citing works
Two novel loci, COBL and SLC10A2, for Alzheimer's disease in African Americans
Mez, J. B., Chung, J., Jun, G., Kriegel, J., Bourlas, A. P., Sherva, R., Logue, M. W., Barnes, L. L., Bennett, D. A., Buxbaum, J. D., et al. (2016) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.09.002 · Cited by 96 · see citing works
Proitsi, P., Lupton, M. K., Velayudhan, L., Newhouse, S., Fogh, I., Tsolaki, M., Daniilidou, M., Pritchard, M., Kłoszewska, I., Soininen, H. S., et al. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001713 · Cited by 94 · see citing works
Self-citationMarschall, H., MATERN, H., Wietholtz, H., et al. (1992) · Journal of Clinical Investigation · doi:10.1172/jci115806 · Cited by 91 · see citing works
Global Metabolic Shifts in Age and Alzheimer’s Disease Mouse Brains Pivot at NAD + /NADH Redox Sites
Dong, Y., & Brewer, G. J. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190408 · Cited by 90 · see citing works
Genome-Wide Association Study of Metabolic Traits Reveals Novel Gene-Metabolite-Disease Links
Rueedi, R., Ledda, M., Nicholls, A. W., Salek, R. M., Marques-Vidal, P. M., Morya, E., Sameshima, K., Montoliu, I., Silva, L. D., Collino, S., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004132 · Cited by 88 · see citing works
Realising the Potential of Urine and Saliva as Diagnostic Tools in Sport and Exercise Medicine
Lindsay, A., & Costello, J. T. (2016) · Sports Medicine · doi:10.1007/s40279-016-0558-1 · Cited by 85 · see citing works
Reitz, C., Tosto, G., Vardarajan, B. N., Rogaeva, E. A., Ghani, M., Rogers, R. S., Conrad, C., Haines, J. L., Pericak‐Vance, M. A., Fallin, M. D., et al. (2013) · Translational Psychiatry · doi:10.1038/tp.2013.13 · Cited by 80 · see citing works
Biomarkers in Amyloid-β Immunotherapy Trials in Alzheimer’s Disease
Blennow, K., Hampel, H. J., & Zetterberg, H. (2013) · Neuropsychopharmacology · doi:10.1038/npp.2013.154 · Cited by 79 · see citing works
Vps10 Family Proteins and the Retromer Complex in Aging-Related Neurodegeneration and Diabetes
Lane, R. F., George‐Hyslop, P. S., Hempstead, B. L., Small, S. A., Strittmatter, S. M., & Gandy, S. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.3359-12.2012 · Cited by 77 · see citing works
Lista, S., Garaci, F., Ewers, M., Teipel, S. J., Zetterberg, H., Blennow, K., & Hampel, H. J. (2013) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.04.506 · Cited by 76 · see citing works
Rac1 activation links tau hyperphosphorylation and Aβ dysmetabolism in Alzheimer’s disease
Borin, M., Saraceno, C., Catania, M., Lorenzetto, E., Pontelli, V., Paterlini, A., Fostinelli, S., Avesani, A., Di Fede, G., Zanusso, G., et al. (2018) · Acta Neuropathologica Communications · doi:10.1186/s40478-018-0567-4 · Cited by 68 · see citing works
Genes associated with the progression of neurofibrillary tangles in Alzheimer’s disease
Miyashita, A., Hatsuta, H., Kikuchi, M., Nakaya, A., Saito, Y., Tsukie, T., Hara, N., Ogishima, S., Kitamura, N., Akazawa, K., et al. (2014) · Translational Psychiatry · doi:10.1038/tp.2014.35 · Cited by 65 · see citing works
Ellison, E. M., Abner, E. L., & Lovell, M. A. (2016) · Journal of Neurochemistry · doi:10.1111/jnc.13912 · Cited by 64 · see citing works
Urinary 8-hydroxy-2′-deoxyguanosine level and plasma paraoxonase 1 activity with Alzheimer’s disease
Zengi, O., Karakaş, A., Ergün, U., Şeneş, M., İnan, L. E., & Yücel, D. (2011) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm.2011.792 · Cited by 60 · see citing works
Wang, Y., Li, J., Matye, D. J., Zhang, Y., Dennis, K. L., Ding, W., & Li, T. (2018) · JCI Insight · doi:10.1172/jci.insight.99676 · Cited by 58 · see citing works
Lipid Metabolism by Gut Microbes and Atherosclerosis
Loscalzo, J. (2011) · Circulation Research · doi:10.1161/res.0b013e3182290620 · Cited by 54 · see citing works
Peng, J., Guo, K., Xia, J., Zhou, J., Yang, J., Westaway, D. A., Wishart, D. S., & Li, L. (2014) · Journal of Proteome Research · doi:10.1021/pr500828v · Cited by 53 · see citing works
Ma, L., Chen, J., Wang, R., Han, Y., Zhang, J., Dong, W., Zhang, X., Wu, Y., & Zhao, Z. (2015) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2014.10.011 · Cited by 53 · see citing works
F-box and leucine-rich repeat protein 5 (FBXL5): Sensing intracellular iron and oxygen
Ruiz, J. C., & Bruick, R. K. (2014) · Journal of Inorganic Biochemistry · doi:10.1016/j.jinorgbio.2014.01.015 · Cited by 51 · see citing works
Velenosi, T. J., Thomson, A. B. R., Tonial, N. C., RaoPeters, A. A. E., Mio, M. A., Lajoie, G., Garg, A. X., House, A. A., & Urquhart, B. L. (2019) · Scientific Reports · doi:10.1038/s41598-019-42992-3 · Cited by 51 · see citing works
Fukuhara, K., Ohno, A., Ota, Y., Senoo, Y., Maekawa, K., Okuda, H., Kurihara, M., Okuno, A., Niida, S., Saito, Y., et al. (2013) · Journal of Clinical Biochemistry and Nutrition · doi:10.3164/jcbn.12-118 · Cited by 50 · see citing works
Yu, J., Kong, L. D., Zhang, A., Han, Y., Liu, Z., Sun, H. J., Liu, L., & Wang, X. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00206 · Cited by 48 · see citing works
Sands, C. J., Wolfer, A. M., Correia, G. D. S., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz566 · Cited by 43 · see citing works
Mayo, W., Moal, M. L., & Abrous, D. N. (2001) · Hormones and Behavior · doi:10.1006/hbeh.2001.1677 · Cited by 41 · see citing works
Robinette, S. L., Holmes, E. C., Nicholson, J. K., & Dumas, M. (2012) · Genome Medicine · doi:10.1186/gm329 · Cited by 39 · see citing works
Self-citationAlterations of the volatile metabolome in mouse models of Alzheimer’s disease
Kimball, B. A., Wilson, D. A., & Wesson, D. W. (2016) · Scientific Reports · doi:10.1038/srep19495 · Cited by 39 · see citing works
Wang, C., Cui, Y., Yang, J., Zhang, J., Yuan, D., Wei, Y., Li, Y., Duo, Y., Li, S., Zhu, W., et al. (2014) · Biomarkers · doi:10.3109/1354750x.2014.994036 · Cited by 37 · see citing works
Evaluation of dementia by acrolein, amyloid-β and creatinine
Igarashi, K., Yoshida, M., Waragai, M., & Kashiwagi, K. (2015) · Clinica Chimica Acta · doi:10.1016/j.cca.2015.07.017 · Cited by 36 · see citing works
The identification of novel steroid N-acetylglucosaminides in the urine of pregnant women
Meng, L. J., Griffiths, W. J., & Sjövall, J. (1996) · The Journal of Steroid Biochemistry and Molecular Biology · doi:10.1016/0960-0760(96)00080-5 · Cited by 32 · see citing works
Newton, J. L., Elliott, C., Frith, J., Ghazala, C., Pairman, J., & Jones, D. E. J. (2011) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0b013e3283470256 · Cited by 29 · see citing works
Foy, C. M. L., Nicholas, H., Hollingworth, P., et al. (2007) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.1810 · Cited by 28 · see citing works
Voyle, N., Kim, M., Proitsi, P., Ashton, N. J., Baird, A. L., Bazenet, C. E., Hye, A., Westwood, S., Chung, R., Ward, M. A., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2015.205 · Cited by 26 · see citing works
Self-citationElucidating the Structure of N 1-Acetylisoputreanine: A Novel Polyamine Catabolite in Human Urine
Fitzgerald, B. L., Mahapatra, S., Farmer, D. K., McNeil, M., Casero, R. A., & Belisle, J. T. (2017) · ACS Omega · doi:10.1021/acsomega.7b00872 · Cited by 21 · see citing works
Rajagopal, L., Soni, D., & Meltzer, H. Y. (2018) · Behavioural Brain Research · doi:10.1016/j.bbr.2018.05.005 · Cited by 21 · see citing works
Whiley, L., Chekmeneva, E., Berry, D. J., et al. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b00241 · Cited by 20 · see citing works
Self-citationDastgheib, M., Dehpour, A. R., Heidari, M., & Moezi, L. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2015.08.018 · Cited by 19 · see citing works
Oxidative DNA Damage - the possible use of biomarkers as additional prognostic factors in oncology
Roszkowski, K. (2014) · Frontiers in bioscience · doi:10.2741/4248 · Cited by 19 · see citing works
The Role of Intracellular Trafficking and the Vps10D Receptors In Alzheimer‘s Disease
Reitz, C. (2012) · Future Neurology · doi:10.2217/fnl.12.31 · Cited by 15 · see citing works
Aslibekyan, S. W., Irvin, M. R., Hidalgo, B., Perry, R. T., Jeyarajah, E. J., Garcia, E., SHALAUROVA, I. Y., Hopkins, P. N., Province, M. A., Tiwari, H. K., et al. (2017) · Journal of Nutrition & Intermediary Metabolism · doi:10.1016/j.jnim.2017.03.002 · Cited by 13 · see citing works
The RacGAP β-Chimaerin is essential for cerebellar granule cell migration
Estep, J. A., Wong, W., Wong, Y.-C. E., Loui, B. M., & Riccomagno, M. M. (2018) · Scientific Reports · doi:10.1038/s41598-017-19116-w · Cited by 8 · see citing works
Effect of Age on Pregnanediol Excretion by Men
Romanoff, L. P., Thomas, A., & BAXTER, M. (1970) · Journal of Gerontology · doi:10.1093/geronj/25.2.98 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Microbial-derived metabolites as a risk factor of age-related cognitive decline and dementia
Connell, E., Le Gall, G., Pontifex, M. G., Sami, S., Cryan, J. F., Clarke, G., Müller, M., & Vauzour, D. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00548-6 · Cited by 208 · see citing works
Buccellato, F. R., D’Anca, M., Fenoglio, C., Scarpini, E., & Galimberti, D. (2021) · Antioxidants · doi:10.3390/antiox10091353 · Cited by 179 · see citing works
Yulug, B., Altay, O., Li, X., Hanoglu, L., Cankaya, S., Lam, S., Velioglu, H. A., Yang, H., Coskun, E., Idil, E., et al. (2023) · Translational Neurodegeneration · doi:10.1186/s40035-023-00336-2 · Cited by 79 · see citing works
- Authors
- Kurbatova, N., Garg, M., Whiley, L., Chekmeneva, E., Jiménez, B., Gómez-Romero, M., Pearce, J., Kimhofer, T., D’Hondt, E., Soininen, H., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B., Aarsland, D., Nevado-Holgado, A., Liu, B., Snowden, S., Proitsi, P., Ashton, N. J., Hye, A., Legido-Quigley, C., Lewis, M. R., Nicholson, J. K., Holmes, E., Brazma, A., & Lovestone, S.
- Affiliations
- European Bioinformatics Institute (GB)
- Hammersmith Hospital (GB)
- Imperial College London (GB)
- IMEC (BE)
- University of Eastern Finland (FI)
- Medical University of Lodz (PL)
- University of Perugia (IT)
- Aristotle University of Thessaloniki (GR)
- Inserm (FR)
- King's College London (GB)
- Warneford Hospital (GB)
- Steno Diabetes Centers (DK)
- Murdoch University (AU)
- Corresponding author
- Kurbatova, Natalja
- ORCID
- Kurbatova, Natalja
- Garg, Manik
- Whiley, Luke
- Chekmeneva, Elena
- Jiménez, Beatriz
- Pearce, Jake
- Kimhofer, Torben
- D’Hondt, Ellie
- Soininen, Hilkka
- Mecocci, Patrizia
- Tsolaki, Magda
- Vellas, Bruno
- Aarsland, Dag
- Proitsi, Petroula
- Ashton, Nicholas J.
- Hye, Abdul
- Lewis, Matthew R.
- Nicholson, Jeremy K.
- Holmes, Elaine
- Lovestone, Simon
- Venue
- Scientific Reports
- Published
- 2020-12-10
- Volume
- 10
- Issue
- 1
- Pages
- 21745-21745
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7730184
- OpenAlex
- W3111765626
- Cited by
- 44
- References
- 95
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 5 · 2023: 10 · 2022: 9 · 2021: 8
- FWCI
- 2.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Murdoch University Research Portal (submittedVersion)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Innovative Medicines Initiative (115372)
- Medical Research Council, UK, and National Institute for Health Research, England (MC_PC_12025)
- Projekt DEAL
- 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
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, mild cognitive impairment, urinary metabolome, metabolic phenotyping, disease biomarkers, early diagnosis, random forest classification, NMR spectroscopy, UHPLC-MS
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Female, Humans, Male, Phenotype, Biomarkers, Quantitative Trait Loci, Metabolomics, Cognitive Dysfunction
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
