Bioenergetic and vascular predictors of potential super-ager and cognitive decline trajectories—a UK Biobank Random Forest classification study

Dempster FN. The rise and fall of the inhibitory mechanism: Toward a unified theory of cognitive development and aging. Dev Rev. 1992;1;12(1):45–75. https://doi.org/10.1016/0273-2297(92)90003-K.

Parkin AJ, Walter BM. Recollective experience, normal aging, and frontal dysfunction. Psychol Aging. 1992;7(2):290. https://doi.org/10.1037/0882-7974.7.2.290.

Salthouse TA. When does age-related cognitive decline begin? Neurobiol Aging. 2009;30(4):507–14. https://doi.org/10.1016/j.neurobiolaging.2008.09.023 (Elsevier Inc).

Article  PubMed  PubMed Central  Google Scholar 

Singh-Manoux A, Kivimaki M, Glymour MM, Elbaz A, Berr C, Ebmeier KP, Ferrie JE, Dugravot A. Timing of onset of cognitive decline: results from Whitehall II prospective cohort study. BMJ. 2012;344. https://doi.org/10.1136/bmj.d7622.

Jensen, Arthur R. Abilities: their structure, growth, and action. Am J Psychol. 1974;290–6. https://doi.org/10.2307/1422024.

Cornelis MC, Wang Y, Holland T, Agarwal P, Weintraub S, Morris MC. Age and cognitive decline in the UK Biobank. PloS One. 2019;14(3):e0213948. https://doi.org/10.1371/journal.pone.0213948.

Kievit RA, Davis SW, Mitchell DJ, Taylor JR, Duncan J, Henson RN. Distinct aspects of frontal lobe structure mediate age-related differences in fluid intelligence and multitasking. Nat Commun. 2014;5(1):5658. https://doi.org/10.1038/ncomms6658.

Lyall DM, Cullen B, Allerhand M, Smith DJ, Mackay D, Evans J, Anderson J, Fawns-Ritchie C, McIntosh AM, Deary IJ, Pell JP. Cognitive test scores in UK Biobank: data reduction in 480,416 participants and longitudinal stability in 20,346 participants. PloS One. 2016;11(4):e0154222. https://doi.org/10.1371/journal.pone.0154222.

Schretlen D, et al. Elucidating the contributions of processing speed, executive ability, and frontal lobe volume to normal age-related differences in fluid intelligence. J Int Neuropsychol Soc. 2000;6(1):52–61. https://doi.org/10.1017/S1355617700611062.

CAS  Article  PubMed  Google Scholar 

Harrison TM, Weintraub S, Mesulam MM, Rogalski E. Superior memory and higher cortical volumes in unusually successful cognitive aging. J Int Neuropsychol Soc. 2012;18(6):1081–5. https://doi.org/10.1017/S1355617712000847.

Article  PubMed  PubMed Central  Google Scholar 

Harrison TM, Maass A, Baker SL, Jagust WJ. Brain morphology, cognition, and β-amyloid in older adults with superior memory performance. Neurobiol Aging. 2018;67:162–70. https://doi.org/10.1016/j.neurobiolaging.2018.03.024.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Zhang J, Andreano JM, Dickerson BC, Touroutoglou A, Barrett LF. Stronger functional connectivity in the default mode and salience networks is associated with youthful memory in superaging. Cereb Cortex. 2020;30(1):72–84. https://doi.org/10.1093/cercor/bhz071.

Article  PubMed  Google Scholar 

Rowe JW, Kahn RL. Human aging: usual and successful. Science. 1987;237(4811):143–9. https://doi.org/10.1126/science.3299702.

Rogalski EJ, Gefen T, Shi J, Samimi M, Bigio E, Weintraub S, Geula C, Mesulam MM. Youthful memory capacity in old brains: anatomic and genetic clues from the Northwestern SuperAging Project. J Cogn Neurosci. 2013;25(1):29–36. https://doi.org/10.1162/jocn_a_00300.

Winchester LM, Powell J, Lovestone S, Nevado-Holgado AJ. Red blood cell indices and anaemia as causative factors for cognitive function deficits and for Alzheimer’s disease. Genome Med. 2018;10(1):1–2. https://doi.org/10.1186/s13073-018-0556-z.

Tao Wang R, Jin D, Li Y, Cheng Liang Q. Decreased mean platelet volume and platelet distribution width are associated with mild cognitive impairment and Alzheimer’s disease. J Psychiatric Res. 2013;47(5):644–9. https://doi.org/10.1016/j.jpsychires.2013.01.014.

Article  Google Scholar 

Sun D, Wang Q, Kang J, Zhou J, Qian R, Wang W, Wang H, Zhang Q. Correlation between serum platelet count and cognitive function in patients with atrial fibrillation: a cross-sectional study. Cardiol Res Pract. 2021;2021. https://doi.org/10.1155/2021/9039610.

Horgusluoglu E, Neff R, Song WM, Wang M, Wang Q, Arnold M, Krumsiek J, Galindo‐Prieto B, Ming C, Nho K, Kastenmüller G. Integrative metabolomics‐genomics approach reveals key metabolic pathways and regulators of Alzheimer’s disease. Alzheimer’s & Dementia. 2022;18(6):1260–78. https://doi.org/10.1002/alz.12468.

Clark AL, Weigand AJ, Bangen KJ, Thomas KR, Eglit GM, Bondi MW, Delano‐Wood L, Alzheimer's Disease Neuroimaging Initiative. Higher cerebrospinal fluid tau is associated with history of traumatic brain injury and reduced processing speed in Vietnam‐era veterans: A Department of Defense Alzheime’s Disease Neuroimaging Initiative (DOD‐ADNI) study. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring. 2021;13(1):e12239. https://doi.org/10.1002/dad2.12239.

Zhang S, Liu YQ, Jia C, Lim YJ, Feng G, Xu E, Long H, Kimura Y, Tao Y, Zhao C, Wang C. Mechanistic basis for receptor-mediated pathological α-synuclein fibril cell-to-cell transmission in Parkinson’s disease. Proceedings of the National Academy of Sciences. 2021;118(26):e2011196118. https://doi.org/10.1073/pnas.2011196118.

Du Y, et al. Plasma metabolites were associated with spatial working memory in major depressive disorder. Med. 2021;100(8):e24581. https://doi.org/10.1097/MD.0000000000024581.

CAS  Article  Google Scholar 

Ooi TC, et al. Intermittent fasting enhanced the cognitive function in older adults with mild cognitive impairment by inducing biochemical and metabolic changes: a 3-year progressive study. Nutrients. 2020;12(9):1–20. https://doi.org/10.3390/nu12092644.

CAS  Article  Google Scholar 

Hearps AC, et al. Aging is associated with chronic innate immune activation and dysregulation of monocyte phenotype and function. Aging Cell. 2012;11(5):867–75. https://doi.org/10.1111/j.1474-9726.2012.00851.x.

CAS  Article  PubMed  Google Scholar 

Kao TW, Chang YW, Chou CC, Hu J, Yu YH, Kuo HK. White blood cell count and psychomotor cognitive performance in the elderly. Eur J Clin Invest. 2011;41(5):513–20. https://doi.org/10.1111/j.1365-2362.2010.02438.x.

Article  PubMed  Google Scholar 

Serre-Miranda C, Roque S, Santos NC, Portugal-Nunes C, Costa P, Palha JA, Sousa N, Correia-Neves M. Effector memory CD4+ T cells are associated with cognitive performance in a senior population. Neurology-Neuroimmunology Neuroinflammation. 2015;2(1). https://doi.org/10.1212/NXI.0000000000000054.

Wang GY, et al. Associations between immunological function and memory recall in healthy adults. Brain Cogn. 2017;119:39–44. https://doi.org/10.1016/j.bandc.2017.10.002.

Article  PubMed  Google Scholar 

Klinedinst BS, et al. Aging-related changes in fluid intelligence, muscle and adipose mass, and sex-specific immunologic mediation: a longitudinal UK Biobank study. Brain Behav Immun. 2019;82:396–405. https://doi.org/10.1016/j.bbi.2019.09.008.

Article  PubMed  PubMed Central  Google Scholar 

Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, Liu B. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. https://doi.org/10.1371/journal.pmed.1001779.

Armstrong J, et al. Dynamic linkage of covid-19 test results between public health England’s second generation surveillance system and UK biobank. Microbial Genomics. 2020;6(7):1–9. https://doi.org/10.1099/mgen.0.000397.

CAS  Article  Google Scholar 

Hilton B, Wilson D, O’Connell AM, Ironmonger D, Rudkin JK, Allen N, Oliver I, Wyllie D. Incidence of microbial infections in English UK Biobank participants: Comparison with the general population. medRxiv. 2020. https://doi.org/10.1101/2020.03.18.20038281.

Würtz P, Kangas AJ, Soininen P, Lawlor DA, Davey Smith G, Ala-Korpela M. Quantitative serum nuclear magnetic resonance metabolomics in large-scale epidemiology: a primer on -omic technologies. Am J Epidemiol. 2017;186(9):1084–96. https://doi.org/10.1093/aje/kwx016.

Article  PubMed  PubMed Central  Google Scholar 

Soininen P, et al. High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst. 2009;134(9):1781–5. https://doi.org/10.1039/b910205a.

CAS  Article  PubMed  Google Scholar 

Kotsiantis SB, Zaharakis ID, Pintelas PE. Machine learning: a review of classification and combining techniques. Artif Intell Rev. 2006;26(3):159–90. https://doi.org/10.1007/s10462-007-9052-3.

Article  Google Scholar 

Liu Y, Wang Y, Zhang J. New Machine Learning Algorithm: Random Forest. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2012;7473(LNCS):246–52. https://doi.org/10.1007/978-3-642-34062-8_32.

Article  Google Scholar 

Loh WY. Classification and regression trees. Wiley Interdiscip Rev: Data Mining Knowl Discov. 2011;1(1):14–23. https://doi.org/10.1002/widm.8.

Article  Google Scholar 

Liu M, Wang M, Wang J, Li D. Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: application to the recognition of orange beverage and Chinese vinegar. Sens Actuators, B Chem. 2013;177:970–80. https://doi.org/10.1016/j.snb.2012.11.071.

CAS  Article  Google Scholar 

Mqadi NM, Naicker N, Adeliyi T. Solving misclassification of the credit card imbalance problem using near miss. Math Probl Eng. 2021;2021. https://doi.org/10.1155/2021/7194728.

Singhal R, Rana R. Chi-square test and its application in hypothesis testing. J Prac Card Sci. 2015;1(1):69. https://doi.org/10.4103/2395-5414.157577.

Article  Google Scholar 

Van Rossum G, Drake FL. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace; 2009.

R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria; 2016. https://www.R-project.org/.

Snowdon DA, Greiner LH, Mortimer JA, Riley KP, Greiner PA, Markesbery WR. Brain infarction and the clinical expression of Alzheimer disease: the Nun Study. Jama. 1997;277(10):813–7. https://doi.org/10.1001/jama.1997.03540340047031.

Cipollini V, Troili F, Giubilei F. Emerging biomarkers in vascular cognitive impairment and dementia: from pathophysiological pathways to clinical application. Int J Mol Sci. 2019;20(11):2812. https://doi.org/10.3390/ijms20112812.

CAS  Article  PubMed Central  Google Scholar 

Stellos K, Panagiota V, Kögel A, Leyhe T, Gawaz M, Laske C. Predictive value of platelet activation for the rate of cognitive decline in Alzheimer’s disease patients. J Cereb Blood Flow Metab. 2010;30(11):1817–20. https://doi.org/10.1038/jcbfm.2010.140.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Krauss RM. Lipoprotein subfractions and cardiovascular disease risk. Curr Opin Lipidol. 2010;21(4):305–11. https://doi.org/10.1097/MOL.0b013e32833b7756.

CAS  Article  PubMed  Google Scholar 

Rizzo M, Berneis K. Low-density lipoprotein size and cardiovascular risk assessment. QJM - Monthly J Assoc Phys. 2006;99(1):1–14. https://doi.org/10.1093/qjmed/hci154.

CAS  Article  Google Scholar 

Tribble DL, Van Den Berg JJ, Motchnik PA, Ames BN, Lewis DM, Chait A, Krauss RM. Oxidative susceptibility of low density lipoprotein subfractions is related to their ubiquinol-10 and alpha-tocopherol content. Proceedings of the National Academy of Sciences. 1994;91(3):1183–7. https://doi.org/10.1073/pnas.91.3.1183.

Steinbrecher UP, Parthasarathy S, Leake DS, Witztum JL, Steinberg D. Modification of low density lipoprotein by endothelial cells involves lipid peroxidation and degradation of low density lipoprotein phospholipids. Proceedings of the National Academy of Sciences. 1984;81(12):3883–7. https://doi.org/10.1073/pnas.81.12.3883.

Ohmura H, et al. Lipid compositional differences of small, dense low-density lipoprotein particle influence its oxidative susceptibility: possible implication of increased risk of coronary artery disease in subjects with phenotype B. Metab Clin Exp. 2002;51(9):1081–7. https://doi.org/10.1053/meta.2002.34695.

CAS  Article  PubMed  Google Scholar 

Vlaardingerbroek H, et al. Essential polyunsaturated fatty acids in plasma and erythrocytes of children with inborn errors of amino acid metabolism. Mol Genet Metab. 2006;88(2):159–65. https://doi.org/10.1016/j.ymgme.2006.01.012.

CAS  Article  PubMed  Google Scholar 

Lauritzen L, Brambilla P, Mazzocchi A, Harsløf LB, Ciappolino V, Agostoni C. DHA effects in brain development and function. Nutrients. 2016;8(1):6. https://doi.org/10.3390/nu8010006.

CAS  Article  PubMed Central  Google Scholar 

Allen PW, Bowen HJ, Sutton LE, Bastiansen O. The molecular structure of acetone. Transactions of the Faraday Society. 1952;48:991–5.

CAS  Article  Google Scholar 

Balasse EO, Féry F. Ketone body production and disposal: effects of fasting, diabetes, and exercise. Diabetes/Metabolism Reviews. 1989;5(3):247–70. https://doi.org/10.1002/dmr.5610050304.

CAS  Article  PubMed  Google Scholar 

McNally MA, Hartman AL. Ketone bodies in epilepsy. J Neurochem. 2012;121(1):28–35. https://doi.org/10.1111/j.1471-4159.2012.07670.x.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Lefèvre A, Adler H, Lieber CS. Effect of ethanol on ketone metabolism. J Clin Investig. 1970;49(10):1775–82. https://doi.org/10.1172/JCI106395.

Article  PubMed  PubMed Central  Google Scholar 

Baraona E, Lieber CS. Effects of ethanol on lipid metabolism. J Lipid Res. 1979;20(3):289–315. https://doi.org/10.1016/s0022-2275(20)40613-3.

CAS  Article  PubMed 

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