Evidence in support of chromosomal sex influencing plasma based metabolome vs APOE genotype influencing brain metabolome profile in humanized APOE male and female mice
Autoři:
Yuan Shang aff001; Aarti Mishra aff001; Tian Wang aff001; Yiwei Wang aff001; Maunil Desai aff002; Shuhua Chen aff001; Zisu Mao aff001; Loi Do aff003; Adam S. Bernstein aff004; Theodore P. Trouard aff003; Roberta D. Brinton aff001
Působiště autorů:
Center for Innovation in Brain Science, University of Arizona, Tucson, Arizona, United States of America
aff001; School of Pharmacy, University of Southern California, Los Angeles, California, United States of America
aff002; Biomedical Engineering, University of Arizona, Tucson, Arizona, United States of America
aff003; College of Medicine, University of Arizona, Tucson, Arizona, United States of America
aff004
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0225392
Souhrn
Late onset Alzheimer’s disease (LOAD) is a progressive neurodegenerative disease with four well-established risk factors: age, APOE4 genotype, female chromosomal sex, and maternal history of AD. Each risk factor impacts multiple systems, making LOAD a complex systems biology challenge. To investigate interactions between LOAD risk factors, we performed multiple scale analyses, including metabolomics, transcriptomics, brain magnetic resonance imaging (MRI), and beta-amyloid assessment, in 16 months old male and female mice with humanized human APOE3 (hAPOE3) or APOE4 (hAPOE4) genes. Metabolomic analyses indicated a sex difference in plasma profile whereas APOE genotype determined brain metabolic profile. Consistent with the brain metabolome, gene and pathway-based RNA-Seq analyses of the hippocampus indicated increased expression of fatty acid/lipid metabolism related genes and pathways in both hAPOE4 males and females. Further, female transcription of fatty acid and amino acids pathways were significantly different from males. MRI based imaging analyses indicated that in multiple white matter tracts, hAPOE4 males and females exhibited lower fractional anisotropy than their hAPOE3 counterparts, suggesting a lower level of white matter integrity in hAPOE4 mice. Consistent with the brain metabolomic and transcriptomic profile of hAPOE4 carriers, beta-amyloid generation was detectable in 16-month-old male and female brains. These data provide therapeutic targets based on chromosomal sex and APOE genotype. Collectively, these data provide a framework for developing precision medicine interventions during the prodromal phase of LOAD, when the potential to reverse, prevent and delay LOAD progression is greatest.
Klíčová slova:
Amino acid analysis – Amino acid metabolism – Gene expression – Glucose metabolism – Lipid metabolism – Metabolomics – principal component analysis – Transcriptome analysis
Zdroje
1. Corder E, Saunders A, Strittmatter W, Schmechel D, Gaskell P, Small G, et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science. 1993;261(5123):921–3. doi: 10.1126/science.8346443 8346443
2. Poirier J, Bertrand P, Kogan S, Gauthier S, Davignon J, Bouthillier D. Apolipoprotein E polymorphism and Alzheimer’s disease. The Lancet. 1993;342(8873):697–9.
3. Saunders AM, Strittmatter WJ, Schmechel D, George-Hyslop PS, Pericak-Vance MA, Joo S, et al. Association of apolipoprotein E allele ϵ4 with late‐onset familial and sporadic Alzheimer’s disease. Neurology. 1993;43(8):1467-. doi: 10.1212/wnl.43.8.1467 8350998
4. Rebeck GW, Reiter JS, Strickland DK, Hyman BT. Apolipoprotein E in sporadic Alzheimer’s disease: allelic variation and receptor interactions. Neuron. 1993;11(4):575–80. doi: 10.1016/0896-6273(93)90070-8 8398148
5. Carrieri G, Bonafè M, De Luca M, Rose G, Varcasia O, Bruni A, et al. Mitochondrial DNA haplogroups and APOE4 allele are non-independent variables in sporadic Alzheimer’s disease. Human genetics. 2001;108(3):194–8. doi: 10.1007/s004390100463 11354629
6. Maruszak A, Safranow K, Branicki W, Gaweda-Walerych K, Pospiech E, Gabryelewicz T, et al. The impact of mitochondrial and nuclear DNA variants on late-onset Alzheimer’s disease risk. Journal of Alzheimer’s disease: JAD. 2011;27(1):197–210. Epub 2011/07/30. doi: 10.3233/JAD-2011-110710 21799244.
7. Edland SD, Tobe VO, Rieder MJ, Bowen JD, McCormick W, Teri L, et al. Mitochondrial genetic variants and Alzheimer disease: a case-control study of the T4336C and G5460A variants. Alzheimer disease and associated disorders. 2002;16(1):1–7. Epub 2002/03/08. doi: 10.1097/00002093-200201000-00001 11882743.
8. Coto E, Gomez J, Alonso B, Corao AI, Diaz M, Menendez M, et al. Late-onset Alzheimer’s disease is associated with mitochondrial DNA 7028C/haplogroup H and D310 poly-C tract heteroplasmy. Neurogenetics. 2011;12(4):345–6. Epub 2011/08/09. doi: 10.1007/s10048-011-0295-4 21822896.
9. Brookmeyer R, Gray S, Kawas C. Projections of Alzheimer’s disease in the United States and the public health impact of delaying disease onset. American journal of public health. 1998;88(9):1337–42. Epub 1998/09/16. doi: 10.2105/ajph.88.9.1337 9736873.
10. Seshadri S, Beiser A, Kelly-Hayes M, Kase CS, Au R, Kannel WB, et al. The lifetime risk of stroke: estimates from the Framingham Study. Stroke; a journal of cerebral circulation. 2006;37(2):345–50. Epub 2006/01/07. doi: 10.1161/01.STR.0000199613.38911.b2 16397184.
11. Paganini-Hill A, Henderson VW. Estrogen deficiency and risk of Alzheimer’s disease in women. American journal of epidemiology. 1994;140(3):256–61. Epub 1994/08/01. doi: 10.1093/oxfordjournals.aje.a117244 8030628.
12. Brinton RD. The healthy cell bias of estrogen action: mitochondrial bioenergetics and neurological implications. Trends in neurosciences. 2008;31(10):529–37. Epub 2008/09/09. doi: 10.1016/j.tins.2008.07.003 18774188.
13. Brookmeyer R, Evans DA, Hebert L, Langa KM, Heeringa SG, Plassman BL, et al. National estimates of the prevalence of Alzheimer’s disease in the United States. Alzheimer’s & dementia: the journal of the Alzheimer’s Association. 2011;7(1):61–73. Epub 2011/01/25. doi: 10.1016/j.jalz.2010.11.007 21255744
14. Riedel BC, Thompson PM, Brinton RD. Age, APOE and sex: Triad of risk of Alzheimer’s disease. The Journal of Steroid Biochemistry and Molecular Biology. 2016;160:134–47. https://doi.org/10.1016/j.jsbmb.2016.03.012 26969397
15. Blass JP. Brain metabolism and brain disease: is metabolic deficiency the proximate cause of Alzheimer dementia? Journal of neuroscience research. 2001;66(5):851–6. Epub 2001/12/18. doi: 10.1002/jnr.10087 11746411.
16. Cunnane S, Nugent S, Roy M, Courchesne-Loyer A, Croteau E, Tremblay S, et al. Brain fuel metabolism, aging, and Alzheimer’s disease. Nutrition (Burbank, Los Angeles County, Calif). 2011;27(1):3–20. Epub 2010/11/03. doi: 10.1016/j.nut.2010.07.021 21035308
17. De Santi S, de Leon MJ, Rusinek H, Convit A, Tarshish CY, Roche A, et al. Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiology of aging. 2001;22(4):529–39. Epub 2001/07/11. doi: 10.1016/s0197-4580(01)00230-5 11445252.
18. Ishii K, Sasaki M, Kitagaki H, Yamaji S, Sakamoto S, Matsuda K, et al. Reduction of cerebellar glucose metabolism in advanced Alzheimer’s disease. Journal of nuclear medicine: official publication, Society of Nuclear Medicine. 1997;38(6):925–8. Epub 1997/06/01. 9189143.
19. Mosconi L, Berti V, Guyara-Quinn C, McHugh P, Petrongolo G, Osorio RS, et al. Perimenopause and emergence of an Alzheimer’s bioenergetic phenotype in brain and periphery. PloS one. 2017;12(10):e0185926. doi: 10.1371/journal.pone.0185926 29016679
20. Mosconi L, Mistur R, Switalski R, Brys M, Glodzik L, Rich K, et al. Declining brain glucose metabolism in normal individuals with a maternal history of Alzheimer disease. Neurology. 2009;72(6):513–20. Epub 2008/11/14. doi: 10.1212/01.wnl.0000333247.51383.43 19005175
21. Mosconi L, De Santi S, Li J, Tsui WH, Li Y, Boppana M, et al. Hippocampal hypometabolism predicts cognitive decline from normal aging. Neurobiology of aging. 2008;29(5):676–92. doi: 10.1016/j.neurobiolaging.2006.12.008 17222480
22. Mosconi L. Glucose metabolism in normal aging and Alzheimer’s disease: Methodological and physiological considerations for PET studies. Clin Transl Imaging. 2013;1(4) doi: 10.1007/s40336-013-0026-y 24409422.
23. Reiman EM, Caselli RJ, Chen K, Alexander GE, Bandy D, Frost J. Declining brain activity in cognitively normal apolipoprotein E epsilon 4 heterozygotes: A foundation for using positron emission tomography to efficiently test treatments to prevent Alzheimer’s disease. Proceedings of the National Academy of Sciences of the United States of America. 2001;98(6):3334–9. Epub 2001/03/15. doi: 10.1073/pnas.061509598 11248079
24. Reiman EM, Chen K, Alexander GE, Caselli RJ, Bandy D, Osborne D, et al. Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer’s dementia. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(1):284–9. Epub 2003/12/23. doi: 10.1073/pnas.2635903100 14688411
25. Reiman EM, Chen K, Alexander GE, Caselli RJ, Bandy D, Osborne D, et al. Correlations between apolipoprotein E epsilon4 gene dose and brain-imaging measurements of regional hypometabolism. Proceedings of the National Academy of Sciences of the United States of America. 2005;102(23):8299–302. Epub 2005/06/04. doi: 10.1073/pnas.0500579102 15932949
26. Mosconi L, Nacmias B, Sorbi S, De Cristofaro MT, Fayazz M, Tedde A, et al. Brain metabolic decreases related to the dose of the ApoE e4 allele in Alzheimer’s disease. Journal of neurology, neurosurgery, and psychiatry. 2004;75(3):370–6. Epub 2004/02/18. doi: 10.1136/jnnp.2003.014993 14966149
27. Mosconi L, Perani D, Sorbi S, Herholz K, Nacmias B, Holthoff V, et al. MCI conversion to dementia and the APOE genotype: a prediction study with FDG-PET. Neurology. 2004;63(12):2332–40. Epub 2004/12/30. doi: 10.1212/01.wnl.0000147469.18313.3b 15623696.
28. Mosconi L, Sorbi S, Nacmias B, De Cristofaro MT, Fayyaz M, Bracco L, et al. Age and ApoE genotype interaction in Alzheimer’s disease: an FDG-PET study. Psychiatry research. 2004;130(2):141–51. Epub 2004/03/23. doi: 10.1016/j.pscychresns.2003.12.005 15033184.
29. Mosconi L, Herholz K, Prohovnik I, Nacmias B, De Cristofaro MT, Fayyaz M, et al. Metabolic interaction between ApoE genotype and onset age in Alzheimer’s disease: implications for brain reserve. Journal of neurology, neurosurgery, and psychiatry. 2005;76(1):15–23. Epub 2004/12/21. doi: 10.1136/jnnp.2003.030882 15607989
30. Mosconi L, De Santi S, Brys M, Tsui WH, Pirraglia E, Glodzik-Sobanska L, et al. Hypometabolism and altered cerebrospinal fluid markers in normal apolipoprotein E E4 carriers with subjective memory complaints. Biological psychiatry. 2008;63(6):609–18. Epub 2007/08/28. doi: 10.1016/j.biopsych.2007.05.030 17720148
31. Valla J, Yaari R, Wolf AB, Kusne Y, Beach TG, Roher AE, et al. Reduced posterior cingulate mitochondrial activity in expired young adult carriers of the APOE epsilon4 allele, the major late-onset Alzheimer’s susceptibility gene. Journal of Alzheimer’s disease: JAD. 2010;22(1):307–13. Epub 2010/09/18. doi: 10.3233/JAD-2010-100129 20847408
32. Wolf AB, Caselli RJ, Reiman EM, Valla J. APOE and neuroenergetics: an emerging paradigm in Alzheimer’s disease. Neurobiology of aging. 2013;34(4):1007–17. Epub 2012/11/20. doi: 10.1016/j.neurobiolaging.2012.10.011 23159550
33. Mosconi L, Berti V, Quinn C, McHugh P, Petrongolo G, Varsavsky I, et al. Sex differences in Alzheimer risk: Brain imaging of endocrine vs chronologic aging. Neurology. 2017;89(13):1382–90. Epub 2017/08/30. doi: 10.1212/WNL.0000000000004425 28855400.
34. Zhao L, Mao Z, Woody SK, Brinton RD. Sex differences in metabolic aging of the brain: insights into female susceptibility to Alzheimer’s disease. Neurobiology of aging. 2016;42:69–79. https://doi.org/10.1016/j.neurobiolaging.2016.02.011 27143423
35. Drzezga A, Riemenschneider M, Strassner B, Grimmer T, Peller M, Knoll A, et al. Cerebral glucose metabolism in patients with AD and different APOE genotypes. Neurology. 2005;64(1):102–7. Epub 2005/01/12. doi: 10.1212/01.WNL.0000148478.39691.D3 15642911.
36. Kish SJ, Mastrogiacomo F, Guttman M, Furukawa Y, Taanman JW, Dozic S, et al. Decreased brain protein levels of cytochrome oxidase subunits in Alzheimer’s disease and in hereditary spinocerebellar ataxia disorders: a nonspecific change? Journal of neurochemistry. 1999;72(2):700–7. Epub 1999/02/04. doi: 10.1046/j.1471-4159.1999.0720700.x 9930743.
37. Chandrasekaran K, Giordano T, Brady DR, Stoll J, Martin LJ, Rapoport SI. Impairment in mitochondrial cytochrome oxidase gene expression in Alzheimer disease. Brain research Molecular brain research. 1994;24(1–4):336–40. Epub 1994/07/01. doi: 10.1016/0169-328x(94)90147-3 7968373.
38. Aksenov MY, Tucker HM, Nair P, Aksenova MV, Butterfield DA, Estus S, et al. The expression of several mitochondrial and nuclear genes encoding the subunits of electron transport chain enzyme complexes, cytochrome c oxidase, and NADH dehydrogenase, in different brain regions in Alzheimer’s disease. Neurochemical research. 1999;24(6):767–74. Epub 1999/08/14. doi: 10.1023/a:1020783614031 10447460.
39. Maurer I, Zierz S, Moller HJ. A selective defect of cytochrome c oxidase is present in brain of Alzheimer disease patients. Neurobiology of aging. 2000;21(3):455–62. Epub 2000/06/20. doi: 10.1016/s0197-4580(00)00112-3 10858595.
40. Parker WD, Filley CM, Parks JK. Cytochrome oxidase deficiency in Alzheimer’s disease. Neurology. 1990;40(8):1302. doi: 10.1212/wnl.40.8.1302 2166249
41. Parker WD Jr., Parks J, Filley CM, Kleinschmidt-DeMasters BK. Electron transport chain defects in Alzheimer’s disease brain. Neurology. 1994;44(6):1090–6. Epub 1994/06/01. doi: 10.1212/wnl.44.6.1090 8208407.
42. Yao J, Rettberg JR, Klosinski LP, Cadenas E, Brinton RD. Shift in brain metabolism in late onset Alzheimer’s disease: implications for biomarkers and therapeutic interventions. Molecular aspects of medicine. 2011;32(4–6):247–57. Epub 2011/10/26. doi: 10.1016/j.mam.2011.10.005 22024249
43. Zhao L, Mao Z, Woody SK, Brinton RD. Sex differences in metabolic aging of the brain: insights into female susceptibility to Alzheimer’s disease. Neurobiology of aging. 2016;42:69–79. doi: 10.1016/j.neurobiolaging.2016.02.011 27143423.
44. Mosconi L. Perimenopause and emergence of an Alzheimer’s bioenergetic phenotype in brain and periphery. 2017;12(10). doi: 10.1371/journal.pone.0185926 29016679
45. Yin F, Yao J, Sancheti H, Feng T, Melcangi RC, Morgan TE, et al. The perimenopausal aging transition in the female rat brain: decline in bioenergetic systems and synaptic plasticity. Neurobiology of aging. 2015;36(7):2282–95. Epub 2015/04/30. doi: 10.1016/j.neurobiolaging.2015.03.013 25921624
46. Gibson GE, Haroutunian V, Zhang H, Park LC, Shi Q, Lesser M, et al. Mitochondrial damage in Alzheimer’s disease varies with apolipoprotein E genotype. Ann Neurol. 2000;48(3):297–303. Epub 2000/09/08. 10976635.
47. Shi L, Du X, Zhou H, Tao C, Liu Y, Meng F, et al. Cumulative effects of the ApoE genotype and gender on the synaptic proteome and oxidative stress in the mouse brain. The international journal of neuropsychopharmacology / official scientific journal of the Collegium Internationale Neuropsychopharmacologicum (CINP). 2014;17(11):1863–79. Epub 2014/05/09. doi: 10.1017/s1461145714000601 24810422.
48. Xu PT, Li YJ, Qin XJ, Scherzer CR, Xu H, Schmechel DE, et al. Differences in apolipoprotein E3/3 and E4/4 allele-specific gene expression in hippocampus in Alzheimer disease. Neurobiology of disease. 2006;21(2):256–75. Epub 2005/10/04. doi: 10.1016/j.nbd.2005.07.004 16198584.
49. Xu PT, Li YJ, Qin XJ, Kroner C, Green-Odlum A, Xu H, et al. A SAGE study of apolipoprotein E3/3, E3/4 and E4/4 allele-specific gene expression in hippocampus in Alzheimer disease. Molecular and cellular neurosciences. 2007;36(3):313–31. Epub 2007/09/08. doi: 10.1016/j.mcn.2007.06.009 17822919
50. Chen HK, Ji ZS, Dodson SE, Miranda RD, Rosenblum CI, Reynolds IJ, et al. Apolipoprotein E4 domain interaction mediates detrimental effects on mitochondria and is a potential therapeutic target for Alzheimer disease. The Journal of biological chemistry. 2011;286(7):5215–21. Epub 2010/12/02. doi: 10.1074/jbc.M110.151084 21118811
51. Snowden SG, Ebshiana AA, Hye A, An Y, Pletnikova O, O’Brien R, et al. Association between fatty acid metabolism in the brain and Alzheimer disease neuropathology and cognitive performance: A nontargeted metabolomic study. PLOS Medicine. 2017;14(3):e1002266. doi: 10.1371/journal.pmed.1002266 28323825
52. Montine TJ, Morrow JD. Fatty Acid Oxidation in the Pathogenesis of Alzheimer’s Disease. The American journal of pathology. 2005;166(5):1283–9 doi: 10.1016/S0002-9440(10)62347-4 15855630
53. Liu Q, Zhang J. Lipid metabolism in Alzheimer’s disease. Neuroscience bulletin. 2014;30(2):331–45. Epub 2014/04/15. doi: 10.1007/s12264-013-1410-3 24733655.
54. Klosinski LP, Yao J, Yin F, Fonteh AN, Harrington MG, Christensen TA, et al. White Matter Lipids as a Ketogenic Fuel Supply in Aging Female Brain: Implications for Alzheimer’s Disease. EBioMedicine. 2015;2(12):1888–904. Epub 2016/02/05. doi: 10.1016/j.ebiom.2015.11.002 26844268
55. Brinton RD. FUELING THE GLUCOSE-STARVED ALZHEIMER’S BRAIN: CATABOLISM OF WHITE MATTER IN THE BRAIN TO GENERATE KETONE BODIES. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2017;13(7):P882–P3.
56. Han X, D MH, McKeel DW Jr., Kelley J, Morris JC. Substantial sulfatide deficiency and ceramide elevation in very early Alzheimer’s disease: potential role in disease pathogenesis. Journal of neurochemistry. 2002;82(4):809–18. Epub 2002/10/03. doi: 10.1046/j.1471-4159.2002.00997.x 12358786.
57. Wood JA, Wood PL, Ryan R, Graff-Radford NR, Pilapil C, Robitaille Y, et al. Cytokine indices in Alzheimer’s temporal cortex: no changes in mature IL-1 beta or IL-1RA but increases in the associated acute phase proteins IL-6, alpha 2-macroglobulin and C-reactive protein. Brain Res. 1993;629(2):245–52. Epub 1993/12/03. doi: 10.1016/0006-8993(93)91327-o 7509248.
58. Kang J, Rivest S. Lipid Metabolism and Neuroinflammation in Alzheimer’s Disease: A Role for Liver X Receptors. Endocrine Reviews. 2012;33(5):715–46. doi: 10.1210/er.2011-1049 22766509
59. Chong J, Yamamoto M, Xia J. MetaboAnalystR 2.0: From Raw Spectra to Biological Insights. Metabolites. 2019;9(3):57. doi: 10.3390/metabo9030057 30909447
60. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research. 2015;43(7):e47–e. doi: 10.1093/nar/gkv007 25605792
61. Steadman PE, Ellegood J, Szulc KU, Turnbull DH, Joyner AL, Henkelman RM, et al. Genetic effects on cerebellar structure across mouse models of autism using a magnetic resonance imaging atlas. Autism research: official journal of the International Society for Autism Research. 2014;7(1):124–37. Epub 2013/10/24. doi: 10.1002/aur.1344 24151012
62. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. NeuroImage. 2012;62(2):782–90. Epub 2011/10/08. doi: 10.1016/j.neuroimage.2011.09.015 21979382.
63. Chen NK, Chang HC, Bilgin A, Bernstein A, Trouard TP. A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI. PloS one. 2018;13(4):e0195952. Epub 2018/04/26. doi: 10.1371/journal.pone.0195952 29694400
64. Smith DS, Li X, Arlinghaus LR, Yankeelov TE, Welch EB. DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis. PeerJ. 2015;3:e909. Epub 2015/04/30. doi: 10.7717/peerj.909 25922795
65. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophysical journal. 1994;66(1):259–67. Epub 1994/01/01. doi: 10.1016/S0006-3495(94)80775-1 8130344
66. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods. 2017;14:417. https://www.nature.com/articles/nmeth.4197#supplementary-information. 28263959
67. Soneson C, Love M, Robinson M. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences [version 2; peer review: 2 approved]. F1000Research. 2016;4(1521). doi: 10.12688/f1000research.7563.2 26925227
68. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology. 2014;15(12):550. Epub 2014/12/18. doi: 10.1186/s13059-014-0550-8 25516281
69. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. 2005;102(43):15545–50. doi: 10.1073/pnas.0506580102 16199517
70. Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics. 2003;34(3):267–73. doi: 10.1038/ng1180 12808457
71. Stephens M. False discovery rates: a new deal. Biostatistics. 2016;18(2):275–94. doi: 10.1093/biostatistics/kxw041 27756721
72. Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019;47(D1):D590–d5. Epub 2018/10/16. doi: 10.1093/nar/gky962 30321428
73. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. Epub 1999/12/11. doi: 10.1093/nar/28.1.27 10592173
74. Mosconi L, Berti V, Swerdlow RH, Pupi A, Duara R, de Leon M. Maternal transmission of Alzheimer’s disease: Prodromal metabolic phenotype and the search for genes. Human Genomics. 2010;4(3):170. doi: 10.1186/1479-7364-4-3-170 20368139
75. Brinton RD, Yao J, Yin F, Mack WJ, Cadenas E. Perimenopause as a neurological transition state. Nat Rev Endocrinol. 2015;11(7):393–405. Epub 2015/05/27. doi: 10.1038/nrendo.2015.82 26007613.
76. Toledo JB, Arnold M, Kastenmuller G, Chang R, Baillie RA, Han X, et al. Metabolic network failures in Alzheimer’s disease: A biochemical road map. Alzheimers Dement. 2017;13(9):965–84. Epub 2017/03/28. doi: 10.1016/j.jalz.2017.01.020 28341160
77. St John-Williams L, Blach C, Toledo JB, Rotroff DM, Kim S, Klavins K, et al. Targeted metabolomics and medication classification data from participants in the ADNI1 cohort. Sci Data. 2017;4:170140. Epub 2017/10/19. doi: 10.1038/sdata.2017.140 29039849
78. Haughey NJ, Bandaru VV, Bae M, Mattson MP. Roles for dysfunctional sphingolipid metabolism in Alzheimer’s disease neuropathogenesis. Biochim Biophys Acta. 2010;1801(8):878–86. Epub 2010/05/11. doi: 10.1016/j.bbalip.2010.05.003 20452460
79. Kosicek M, Hecimovic S. Phospholipids and Alzheimer’s disease: alterations, mechanisms and potential biomarkers. International journal of molecular sciences. 2013;14(1):1310–22. Epub 2013/01/12. doi: 10.3390/ijms14011310 23306153
80. Farooqui AA, Horrocks LA, Farooqui T. Interactions between neural membrane glycerophospholipid and sphingolipid mediators: a recipe for neural cell survival or suicide. J Neurosci Res. 2007;85(9):1834–50. Epub 2007/03/30. doi: 10.1002/jnr.21268 17393491.
81. Whiley L, Sen A, Heaton J, Proitsi P, Garcia-Gomez D, Leung R, et al. Evidence of altered phosphatidylcholine metabolism in Alzheimer’s disease. Neurobiol Aging. 2014;35(2):271–8. Epub 2013/09/18. doi: 10.1016/j.neurobiolaging.2013.08.001 24041970
82. Klavins K, Koal T, Dallmann G, Marksteiner J, Kemmler G, Humpel C. The ratio of phosphatidylcholines to lysophosphatidylcholines in plasma differentiates healthy controls from patients with Alzheimer’s disease and mild cognitive impairment. Alzheimers Dement (Amst). 2015;1(3):295–302. Epub 2016/01/09. doi: 10.1016/j.dadm.2015.05.003 26744734
83. Law SH, Chan ML, Marathe GK, Parveen F, Chen CH, Ke LY. An Updated Review of Lysophosphatidylcholine Metabolism in Human Diseases. Int J Mol Sci. 2019;20(5). Epub 2019/03/09. doi: 10.3390/ijms20051149 30845751
84. Mishra A, Brinton RD. Inflammation: Bridging Age, Menopause and APOEε4 Genotype to Alzheimer’s Disease. Frontiers in Aging Neuroscience. 2018;10(312). doi: 10.3389/fnagi.2018.00312 30356809
85. Mathys H, Adaikkan C, Gao F, Young JZ, Manet E, Hemberg M, et al. Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution. Cell reports. 2017;21(2):366–80. Epub 2017/10/12. doi: 10.1016/j.celrep.2017.09.039 29020624
86. Operto G, Cacciaglia R, Grau-Rivera O, Falcon C, Brugulat-Serrat A, Ródenas P, et al. White matter microstructure is altered in cognitively normal middle-aged APOE-ε4 homozygotes. Alzheimer’s Research & Therapy. 2018;10(1):48. doi: 10.1186/s13195-018-0375-x 29793545
87. Honea RA, Vidoni E, Harsha A, Burns JM. Impact of APOE on the healthy aging brain: a voxel-based MRI and DTI study. Journal of Alzheimer’s disease: JAD. 2009;18(3):553–64. Epub 2009/07/09. doi: 10.3233/JAD-2009-1163 19584447
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