[1] Deary IJ. Intelligence. Annu Rev Psychol 2012;63:453-82.
[2] Blackwell LS, Trzesniewski KH, Dweck CS. Implicit theories of intelligence predict achievement across an adolescent transition: a longitudinal study and an intervention. Child Dev 2007;78(1):246-63.
[3] Deary IJ, Strand S, Smith P, Fernandes C. Intelligence and educational achievement. Intelligence 2007;35(1):13-21.
[4] Burks SV, Carpenter JP, Goette L, Rustichini A. Cognitive skills affect economic preferences, strategic behavior, and job attachment. Proc Natl Acad Sci U S A 2009;106(19):7745-50.
[5] Gottfredson LS. Why g matters: The complexity of everyday life. Intelligence 1997;24(1):79-132.
[6] Deary I. Why do intelligent people live longer? Nature 2008;456(7219):175-6.
[7] Plomin R, von Stumm S. The new genetics of intelligence. Nature Reviews Genetics 2018;19(3):148-59.
[8] Panizzon MS, Vuoksimaa E, Spoon KM, Jacobson KC, Lyons MJ, Franz CE, et al. Genetic and Environmental Influences of General Cognitive Ability: Is g a valid latent construct? Intelligence 2014;43:65-76.
[9] Sniekers S, Stringer S, Watanabe K, Jansen PR, Coleman JRI, Krapohl E, et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat Genet 2017;49(7):1107-12.
[10] Savage JE, Jansen PR, Stringer S, Watanabe K, Bryois J, de Leeuw CA, et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet 2018;50(7):912-9.
[11] Nisbett RE, Aronson J, Blair C, Dickens W, Flynn J, Halpern DF, et al. Intelligence: new findings and theoretical developments. Am Psychol 2012;67(2):130-59.
[12] Dehghan A. Chapter 19 - Linking Metabolic Phenotyping and Genomic Information. In: Lindon JC, Nicholson JK, Holmes E, editors. The Handbook of Metabolic Phenotyping: Elsevier; 2019. p. 561-9.
[13] Gieger C, Geistlinger L, Altmaier E, Hrabe de Angelis M, Kronenberg F, Meitinger T, et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet 2008;4(11):e1000282.
[14] Suhre K, Shin S-Y, Petersen A-K, Mohney RP, Meredith D, Wägele B, et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 2011;477(7362):54-60.
[15] Kettunen J, Tukiainen T, Sarin AP, Ortega-Alonso A, Tikkanen E, Lyytikainen LP, et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 2012;44(3):269-76.
[16] Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet 2014;46(6):543-50.
[17] Burgess S, Daniel RM, Butterworth AS, Thompson SG, Consortium E-IA. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int J Epidemiol 2015;44(2):484-95.
[18] Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res 2017;26(5):2333-55.
[19] Cheung C-L, Tan KCB, Au PCM, Li GHY, Cheung BMY. Evaluation of GDF15 as a therapeutic target of cardiometabolic diseases in human: A Mendelian randomization study. EBioMedicine 2019;41:85-90.
[20] Sanna S, van Zuydam NR, Mahajan A, Kurilshikov A, Vich Vila A, Vosa U, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet 2019;51(4):600-5.
[21] Telomeres Mendelian Randomization C, Haycock PC, Burgess S, Nounu A, Zheng J, Okoli GN, et al. Association Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases: A Mendelian Randomization Study. JAMA Oncol 2017;3(5):636-51.
[22] Evans DM, Davey Smith G. Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality. Annu Rev Genomics Hum Genet 2015;16:327-50.
[23] Burgess S, Butterworth A, Thompson SG. Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data. Genet Epidemiol 2013;37(7):658-65.
[24] Davies G, Lam M, Harris SE, Trampush JW, Luciano M, Hill WD, et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat Commun 2018;9(1):2098.
[25] Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 2015;44(2):512-25.
[26] Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 2016;40(4):304-14.
[27] Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018;50(5):693-8.
[28] Bowden J, Del Greco M F, Minelli C, Zhao Q, Lawlor DA, Sheehan NA, et al. Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. International Journal of Epidemiology 2019;48(3):728-42.
[29] Greco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med 2015;34(21):2926-40.
[30] Chong J, Wishart DS, Xia J. Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis. Curr Protoc Bioinformatics 2019;68(1):e86.
[31] Jewison T, Su Y, Disfany FM, Liang Y, Knox C, Maciejewski A, et al. SMPDB 2.0: big improvements to the Small Molecule Pathway Database. Nucleic Acids Res 2014;42(Database issue):D478-84.
[32] Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012;40(Database issue):D109-14.
[33] Ristoff E, Larsson A. Inborn errors in the metabolism of glutathione. Orphanet J Rare Dis 2007;2:16.
[34] Rumping L, Vringer E, Houwen RHJ, van Hasselt PM, Jans JJM, Verhoeven-Duif NM. Inborn errors of enzymes in glutamate metabolism. J Inherit Metab Dis 2019.
[35] McDougall GJ, Jr., Austin-Wells V, Zimmerman T. Utility of nutraceutical products marketed for cognitive and memory enhancement. J Holist Nurs 2005;23(4):415-33.
[36] Grioli S, Lomeo C, Quattropani MC, Spignoli G, Villardita C. Pyroglutamic acid improves the age associated memory impairment. Fundam Clin Pharmacol 1990;4(2):169-73.
[37] Hlozek T, Krizek T, Tuma P, Bursova M, Coufal P, Cabala R. Quantification of paracetamol and 5-oxoproline in serum by capillary electrophoresis: Implication for clinical toxicology. J Pharm Biomed Anal 2017;145:616-20.
[38] Raijmakers R, Egberts WV, van Venrooij WJ, Pruijn GJM. Protein–Protein Interactions between Human Exosome Components Support the Assembly of RNase PH-type Subunits into a Six-membered PNPase-like Ring. Journal of Molecular Biology 2002;323(4):653-63.
[39] Sharma V, Ounallah-Saad H, Chakraborty D, Hleihil M, Sood R, Barrera I, et al. Local Inhibition of PERK Enhances Memory and Reverses Age-Related Deterioration of Cognitive and Neuronal Properties. J Neurosci 2018;38(3):648-58.
[40] Ohno M. PERK as a hub of multiple pathogenic pathways leading to memory deficits and neurodegeneration in Alzheimer's disease. Brain Res Bull 2018;141:72-8.
[41] Janssen CI, Kiliaan AJ. Long-chain polyunsaturated fatty acids (LCPUFA) from genesis to senescence: the influence of LCPUFA on neural development, aging, and neurodegeneration. Prog Lipid Res 2014;53:1-17.
[42] Innis SM. Dietary omega 3 fatty acids and the developing brain. Brain Res 2008;1237:35-43.
[43] Simopoulos AP. Evolutionary aspects of diet: the omega-6/omega-3 ratio and the brain. Mol Neurobiol 2011;44(2):203-15.