PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Alzheimer’s disease (AD) is a progressive and relentless debilitating neurodegenerative disease. A post-mortem microscopic neuropathological examination of the brain revealed the existence of extracellular β-amyloid plaques and intracellular neurofibrillary tangles. An accurate early diagnosis of AD is difficult because various disorders share the initial symptoms of the disease. Based on system biology, the multi-omics approach captures and integrates information from genomics, transcriptomics, proteomics, cytokinomics, and metabolomics. This study developed an AD prediction model based on the integrated blood-based multi-omics dataset involving 32 AD patients and 15 non-AD subjects. The integrated multi-omics dataset consists of 16 transcript genes, 14 metabolites, and nine cytokines. Due to the complete separation and multicollinearity issues, Firth’s logistic regression model was then developed to predict AD using the principal components. The model revealed 18 potential biomarkers of AD, consisting of seven metabolites, two transcriptomes, and nine cytokines. These potential biomarkers show an upregulated risk in the AD group compared to the non-AD subjects. The possibility of using these biomarkers as early predictors of AD is discussed.

• Acal, C., Aguilera, A. M., & Escabias, M. (2020). New modeling approaches based on varimax rotation of functional principal components. Mathematics, 8(11), 1-15. https://doi.org/10.3390/math8112085

• Adnan, N., Ahmad, M. H., & Adnan, R. (2006). A comparative study on some methods for handling multicollinearity problems. Matematika, 22(2), 109-119.

• Azad, F. J., Talaei, A., Rafatpanah, H., & Yousefzadeh, H. (2014). Association between cytokine production and disease severity in Alzheimer’s disease. Iranian Journal of Allergy, Asthma & Immunology, 13(6), 433-439.

• Bavarsad, K., Saadat, S., Roshan, N. M., Hadjzadeh, M. A. R., & Boskabady, M. H. (2020). Effects of levothyroxine on lung inflammation, oxidative stress and pathology in a rat model of Alzheimer’s disease. Respiratory Physiology and Neurobiology, 277, Article 103437. https://doi.org/10.1016/j.resp.2020.103437

• Berdyshev, E. V. (2011). Mass spectrometry of fatty aldehydes. Biochimica et Biophysica Acta - Molecular and Cell Biology of Lipids, 1811(11), 680-693. https://doi.org/10.1016/j.bbalip.2011.08.018

• Bertram, L., McQueen, M. B., Mullin, K., Blacker, D., & Tanzi, R. E. (2007). Systematic meta-analyses of Alzheimer disease genetic association studies: The AlzGene database. Nature Genetics, 39(1), 17-23. https://doi.org/10.1038/ng1934

• Brand, B., Hadlich, F., Brandt, B., Schauer, N., Graunke, K. L., Langbein, J., Repsilber, D., Ponsuksili, S., & Schwerin, M. (2015). Temperament type specific metabolite profiles of the prefrontal cortex and serum in cattle. PloS One, 10(4), Article e0125044. https://doi.org/10.1371/journal.pone.0125044

• Cayton, H., Graham, N., & Warner, J. (2008). Alzheimer’s and other dementias. Class Publishing.

• Clark, C., Dayon, L., Masoodi, M., Bowman, G. L., & Popp, J. (2021). An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer’s disease. Alzheimer’s Research and Therapy, 13(1), 1-19. https://doi.org/10.1186/s13195-021-00814-7

• Clerici, M. (2010). Beyond IL-17: New cytokines in the pathogenesis of HIV infection. Current Opinion in HIV and AIDS, 5(2), 184-188. https://doi.org/10.1097/COH.0b013e328335c23c

• Cummings, J. L., & Jeste, D. V. (1999). Alzheimer’s disease and its management in the year 2010. Psychiatric Services, 50(9), 1173-1177. http://www.ncbi.nlm.nih.gov/pubmed/10478903

• D’Agostino, G., Russo, R., Avagliano, C., Cristiano, C., Meli, R., & Calignano, A. (2012). Palmitoylethanolamide protects against the amyloid-Β25-35-induced learning and memory impairment in mice, an experimental model of alzheimer disease. Neuropsychopharmacology, 37(7), 1784-1792. https://doi.org/10.1038/npp.2012.25

• Dayana, S. M. H., Lim, S. M., Tan, M. P., Chin, A. V, Poi, P. J. H., Kamaruzzaman, S. B., Majeed, A. B. A., & Ramasamy, K. (2014). IP-10 and IL-13 as potentially new, non-classical blood-based cytokine biomarker for Alzheimer’s disease. Neuorology and Neurosciences, 43(April), Article 115. https://doi.org/10.1093/ageing/afu045.2

• Firth, D. (1993). Bias reduction of maximum likelihood estimates. Biometrika, 80(1), 27-38.

• Gomez-Ramirez, J., & Wu, J. (2014). Network-based biomarkers in Alzheimer’s disease: Review and future directions. Frontiers in Aging Neuroscience, 6, Article 12. https://doi.org/10.3389/fnagi.2014.00012

• Gross, A. L., Jones, R. N., Habtemariam, D. A., Fong, T. G., Tommet, D., Quach, L., Schmitt, E., Yap, L., & Inouye, S. K. (2012). Delirium and long-term cognitive trajectory among persons with dementia. Archives of Internal Medicine, 172(17), 1324-1331. https://doi.org/10.1001/archinternmed.2012.3203

• Hasin, Y., Seldin, M., & Lusis, A. (2017). Multi-omics approaches to disease. Genome Biology, 18(1), 1-15. https://doi.org/10.1186/s13059-017-1215-1

• Hasni, D. S. M., Lim, S. M., Chin, A. V., Tan, M. P., Poi, P. J. H., Kamaruzzaman, S. B., Majeed, A. B. A., & Ramasamy, K. (2016). Peripheral cytokines, C-X-C motif ligand10 and interleukin-13, are associated with Malaysian Alzheimer’s disease. Geriatrics and Gerontology International, 17(5), 839-846. https://doi.org/10.1111/ggi.12783

• Heinze, G., & Schemper, M. (2002). A solution to the problem of separation in logistic regression. Statistics in Medicine, 21(16), 2409-2419. https://doi.org/10.1002/sim.1047

• Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.

• Humpel, C. (2011). Identifying and validating biomarkers for Alzheimer’s disease. Trends Biotechnol, 29(1), 26-32. https://doi.org/10.1016/j.tibtech.2010.09.007

• Ibáñez, C., Simó, C., & Cifuentes, A. (2013). Metabolomics in Alzheimer’s disease research. Electrophoresis, 34(19), 2799-2811. https://doi.org/10.1002/elps.201200694

• Jackson, D. A. (1993). Stopping rules in principal components analysis: A comparison of heuristical and statistical approaches. Ecological Society of America, 74(8), 2204-2214.

• Jang, H., Kim, M., Hong, J. Y., Cho, H. J., Kim, C. H., Kim, Y. H., Sohn, M. H., & Kim, K. W. (2020). Mitochondrial and nuclear mitochondrial variants in allergic diseases. Allergy, Asthma and Immunology Research, 12(5), 877-884. https://doi.org/10.4168/aair.2020.12.5.877

• Kocak, E. (2020). Evaluation of ms-dial and mzmine2 softwares for clinical lipidomics analysis. Communications Faculty of Sciences University of Ankara Series, 62(1), 100-114.

• Konis, K. (2007). Linear programming algorithms for detecting separated data in binary logistic regression models (PhD Thesis). University of Oxford, UK.

• Kosmidis, I., & Firth, D. (2010). A generic algorithm for reducing bias in parametric estimation. Electronic Journal of Statistics, 4, 1097-1112. https://doi.org/10.1214/10-EJS579

• Kuehl Jr, F. A, Jacob, T. A, Galey, O. H., Ormond, R. E., & Meisinger, M. A. P. (1957). The identification of N-(2-hydroxyethyl)-palmitamide as a naturally occuring anti-inflammatory agent. Journal of the American Oil Chemists’ Society, 79(8), 5577-5578.

• Kussmann, M., Raymond, F., & Affolter, M. (2006). OMICS-driven biomarker discovery in nutrition and health. Journal of Biotechnology, 124(4), 758-787. https://doi.org/10.1016/j.jbiotec.2006.02.014

• Li, J., Liu, Y., Li, W., Wang, Z., Guo, P., Li, L., & Li, N. (2018). Metabolic profiling of the effects of ginsenoside Re in an Alzheimer’s disease mouse model. Behavioural Brain Research, 337(April 2017), 160-172. https://doi.org/10.1016/j.bbr.2017.09.027

• Li, N. J., Liu, W. T., Li, W., Li, S. Q., Chen, X. H., Bi, K. S., & He, P. (2010). Plasma metabolic profiling of Alzheimer’s disease by liquid chromatography/mass spectrometry. Clinical Biochemistry, 43(12), 992-997. https://doi.org/10.1016/j.clinbiochem.2010.04.072

• Licastro, F., Grimaldi, L. M. E., Bonafè, M., Martina, C., Olivieri, F., Cavallone, L., Giovanietti, S., Masliah, E., & Franceschi, C. (2003). Interleukin-6 gene alleles affect the risk of Alzheimer’s disease and levels of the cytokine in blood and brain. Neurobiology of Aging, 24(7), 921-926. https://doi.org/10.1016/S0197-4580(03)00013-7

• Marioni, R. E., Harris, S. E., Zhang, Q., McRae, A. F., Hagenaars, S. P., Hill, W. D., Davies, G., Ritchie, C. W., Gale, C. R., Starr, J. M., Goate, A. M., Porteous, D. J., Yang, J., Evans, K. L., Deary, I. J., Wray, N. R., & Visscher, P. M. (2018). GWAS on family history of Alzheimer’s disease. Translational Psychiatry, 8(1), 0-6. https://doi.org/10.1038/s41398-018-0150-6

• Maskery, M., Goulding, E. M., Gengler, S., Melchiorsen, J. U., & Rosenkilde, M. M. (2020). The dual GLP-1 / GIP receptor agonist DA4-JC shows superior protective properties compared to the GLP-1 analogue liraglutide in the APP / PS1 mouse model of Alzheimer’s disease. American Journal of Alzheimer’s Disease & Other Dementias, 35, 1-11. https://doi.org/10.1177/1533317520953041

• Minter, M. R., Taylor, J. M., & Crack, P. J. (2016). The contribution of neuroinflammation to amyloid toxicity in Alzheimer’s disease. Journal of Neurochemistry, 136(3), 457-474. https://doi.org/10.1111/jnc.13411

• Mrak, R. E., & Griffin, W. S. T. (2005). Potential inflammatory biomarkers in Alzheimer’s disease. Journal of Alzheimer’s Disease: JAD, 8(4), 369-375.

• Nazarian, A., Yashin, A. I., & Kulminski, A. M. (2020). Summary-based methylome-wide association analyses suggest potential genetically driven epigenetic heterogeneity of Alzheimer’s disease. Journal of Clinical Medicine, 9(5), Article 1489. https://doi.org/10.3390/jcm9051489

• Park, J. C., Han, S. H., & Mook-Jung, I. (2020). Peripheral inflammatory biomarkers in Alzheimer’s disease: A brief review. BMB Reports, 53(1), 10-19. https://doi.org/10.5483/BMBRep.2020.53.1.309

• Rahayu, S., Sugiarto, T., Madu, L., Holiawati, & Subagyo, A. (2017). Application of principal component analysis (PCA) to reduce multicollinearity exchange rate currency of some countries in Asia period 2004-2014. International Journal of Educational Methodology, 3(2), 75-83. https://doi.org/10.12973/ijem.3.2.75

• Rahman, M. S., & Sultana, M. (2017). Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data. BMC Medical Research Methodology, 17(1), 1-15. https://doi.org/10.1186/s12874-017-0313-9

• Romero, R., Espinoza, J., Gotsch, F., Kusanovic, J. P., Friel, L. A., Erez, O., Mazaki‐Tovi, S., Than, N. G., Hassan, S., & Tromp, G. (2006). The use of high‐dimensional biology (genomics, transcriptomics, proteomics, and metabolomics) to understand the preterm parturition syndrome. BJOG: An International Journal of Obstetrics & Gynaecology, 113(s3), 118-135.

• Rosner, B. (1975). On the detection of many outliers. Technometrics, 17(2), 221-227. https://doi.org/10.1080/00401706.1975.10489305

• Rougoor, C. W., Sundaram, R., & Van Arendonk, J. A. M. (2000). The relation between breeding management and 305-day milk production, determined via principal components regression and partial least squares. Livestock Production Science, 66(1), 71-83. https://doi.org/10.1016/S0301-6226(00)00156-1

• Rousseaux, M., Rénier, J., Anicet, L., Pasquier, F., & Mackowiak-Cordoliani, M. A. (2012). Gesture comprehension, knowledge and production in Alzheimer’s disease. European Journal of Neurology, 19(7), 1037-1044. https://doi.org/10.1111/j.1468-1331.2012.03674.x

• Rubio-Perez, J. M., & Morillas-Ruiz, J. M. (2012). A review: Inflammatory process in Alzheimer’s disease, role of cytokines. The Scientific World Journal, 2012, Article 756357. https://doi.org/10.1100/2012/756357

• Siino, M., Fasola, S., & Muggeo, V. M. R. (2018). Inferential tools in penalized logistic regression for small and sparse data: A comparative study. Statistical Methods in Medical Research, 27(5), 1365-1375. https://doi.org/10.1177/0962280216661213

• Sun, L. M., Zhu, B. J., Cao, H. T., Zhang, X. Y., Zhang, Q. C., Xin, G. Z., Pan, L. M., Liu, L. F., & Zhu, H. X. (2018). Explore the effects of Huang-Lian-Jie-Du-Tang on Alzheimer’s disease by UPLC-QTOF/MS-based plasma metabolomics study. Journal of Pharmaceutical and Biomedical Analysis, 151, 75-83. https://doi.org/10.1016/j.jpba.2017.12.053

• Swardfager, W., Lanctot, K., Rothenburg, L., Wong, A., Cappell, J., & Herrmann, N. (2010). A meta-analysis of cytokines in Alzheimer’s disease. Biol Psychiatry, 68(10), 930-941. https://doi.org/10.1016/j.biopsych.2010.06.012

• Tanzi, R. E. (2012). The genetics of Alzheimer disease. Cold Spring Harbor Perspectives in Medicine, 2(10), Article a006296. https://doi.org/10.1101/cshperspect.a006296

• Von Schulze, A. T., Deng, F., Morris, J. K., & Geiger, P. C. (2020). Heat therapy: Possible benefits for cognitive function and the aging brain. Journal of Applied Physiology, 129(6), 1468-1476. https://doi.org/10.1152/japplphysiol.00168.2020

• Waring, S. C., & Rosenberg, R. N. (2008). Genome-wide association studies in Alzheimer disease. Archives of Neurology, 65(3), 329-334. https://doi.org/10.1001/archneur.65.3.329

• Xie, L., Lai, Y., Lei, F., Liu, S., Liu, R., & Wang, T. (2015). Exploring the association between interleukin-1beta and its interacting proteins in Alzheimer’s disease. Molecular Medicine Reports, 11(5), 3219-3228. https://doi.org/10.3892/mmr.2015.3183

• Yin, Y., Liu, Y., Pan, X., Chen, R., Li, P., Wu, H. J., Zhao, Z. Q., Li, Y. P., Huang, L. Q., Zhuang, J. H., & Zhao, Z. X. (2016). Interleukin-1β Promoter polymorphism enhances the risk of sleep disturbance in Alzheimer’s disease. PLoS One, 11(3), 1-13. https://doi.org/10.1371/journal.pone.0149945

• Yin, Z., Raj, D., Saiepour, N., Van Dam, D., Brouwer, N., Holtman, I. R., Eggen, B. J. L., Möller, T., Tamm, J. A., Abdourahman, A., Hol, E. M., Kamphuis, W., Bayer, T. A., De Deyn, P. P., & Boddeke, E. (2017). Immune hyperreactivity of Aβ plaque-associated microglia in Alzheimer’s disease. Neurobiology of Aging, 55, 115-122. https://doi.org/10.1016/j.neurobiolaging.2017.03.021

• Zhang, X. (2011). Omics technologies in cancer biomarker discovery. CRC Press.

• Zheng, C., Zhou, X. W., & Wang, J. Z. (2016). The dual roles of cytokines in Alzheimer’s disease: Update on interleukins, TNF-α, TGF-β and IFN-γ. Translational Neurodegeneration, 5(1), 1-15. https://doi.org/10.1186/s40035-016-0054-4

• Zhou, J., Zhu, Z., & Ji, Z. (2014). A Memetic algorithm based feature weighting for metabolomics data classification. Chinese Journal of Electronics, 23(4), 706-711.