Identification of the common pathogenesis of Alzheimer’s and nonalcoholic fatty liver disease and exploration of their relationship with immune cells
Main Article Content
Keywords
Alzheimer’s Disease, NAFLD, immune Cells, bioinformatics analysis, potential biomarkers
Abstract
Alzheimer’s disease (AD) and nonalcoholic fatty liver disease (NAFLD) are both prominent public health concerns owing to their increasing prevalence and burden on healthcare systems. The interconnected genetic and immunological mechanisms that may cause both of these diseases are poorly understood. This study used broad gene expression datasets to identify similar molecular markers and immunological profiles in AD and NAFLD and evaluate their potential. Using the Gene Expression Omnibus (GEO) database, mRNA expression profiles from patients with AD and NAFLD were analyzed alongside control samples to identify differentially expressed genes (DEGs). Systems biology approaches, including LASSO regression and multivariate logistic regression models, were used to further refine the significance of DEGs. The diagnostic potential of the key genes was evaluated using receiver operating characteristic (ROC) curves, and the immune cell environment was quantified using the Immune Cell Abundance Identifier (ImmuCellAI). We identified 11,278 DEGs, with 3551 upregulated and 7857 downregulated genes. S100A8, CXCL9, and ST8SIA3 have emerged as significant biomarkers of both AD and NAFLD. ROC analysis substantiated the diagnostic value of these markers. Additionally, distinct patterns of immune cell populations have been observed in AD and NAFLD, highlighting potential targets for immunomodulatory therapy. This study elucidates shared molecular and immune mechanisms in AD and NAFLD, offering insights into the pathophysiological underpinnings that could inform the development of novel diagnostic and therapeutic strategies. S100A8, CXCL9, and ST8SIA3 are potential candidates for future clinical application. Further investigation into these genetic discoveries and their immune system effects may lead to a unified strategy for treating these complicated disorders.
References
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13 Jadhav PA, Thomas AB, Chopada VM, Bokaria PV, Deokate SB, Chougule PS, et al. Correlation of non-alcoholic fatty liver disease and neurodegenerative disorders. Egyptian Liver J. 2024;14(1):79. 10.1186/s43066-024-00386-9
14 Hadjihambi A. Cerebrovascular alterations in NAFLD: Is it increasing our risk of Alzheimer’s disease? Anal Biochem. 2022; 636:114387. 10.1016/j.ab.2021.114387
15 Rita M, Giuffrè M, Crocè LS, Gazzin S, Tiribelli C. Nonalcoholic fatty liver disease and altered neuropsychological functions in patients with subcortical vascular dementia. J Pers Med. 2022;12(7):1106. 10.3390/jpm12071106
16 Bulgart HR, Neczypor EW, Wold LE, Mackos AR. Microbial involvement in Alzheimer disease development and progression. Mol Neurodegener. 2020;15(1):42. 10.1186/s13024-020-00378-4
17 Zhang JJ, Shen Y, Chen XY, Jiang ML, Yuan FH, Xie SL, et al. Integrative network-based analysis on multiple Gene Expression Omnibus datasets identifies novel immune molecular markers implicated in non-alcoholic steatohepatitis. Front Endocrinol. 2023; 14:1115890. 10.3389/fendo.2023.1115890.
18 Wolski WE, Nanni P, Grossmann J, d’Errico M, Schlapbach R, Panse C. prolfqua: A comprehensive R-package for proteomics differential expression analysis. J Proteome Res. 2023;22(4):1092–104. 10.1021/acs.jproteome.2c00441
19 Zhao K, Rhee SY. Interpreting omics data with pathway enrichment analysis. Trends Genet. 2023;39(4):308–19. 10.1016/j.tig.2023.01.003
20 Zhang X, Wang Z, Hu L, Shen X, Liu C. Identification of potential genetic biomarkers and target genes of peri-implantitis using bioinformatics tools. BioMed Res Int. 2021; 2021:1759214. 10.1155/2021/1759214
21 Xiang J, Huang W, He Y, Li Y, Wang Y, Chen R. Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis. Front Genet. 2022; 13:1041524. 10.3389/fgene.2022.1041524
22 Xu G, Qi F, Li H, Yang Q, Wang H, Wang X, et al. The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing. Cell Discov. 2020; 6:73. 10.1038/s41421-020-00225-2
23 Tang K, Li X, Mo J, Chen Y, Huang C, Li T, et al. CD69 serves as a potential diagnostic and prognostic biomarker for hepatocellular carcinoma. Sci Rep. 2023;13(1):7452. 10.1038/s41598-023-34261-1
24 Jiang H, Hu Y, Zhang Z, Chen X, Gao J. Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease. Lipids Health Dis. 2023;22(1):150. 10.1186/s12944-023-01911-2
25 Dan C, Jialiang L Taokan H Juan K Changhai J Jun L. Hepatic antioxidant and gut ecological modulation properties of long-term intake of tea (Camellia sinensis L.) flower extract in vivo. Quality Assurance and Safety of Crops & Foods, 2023; 15(3): 11–21. 10.15586/qas.v15i3.1209
26 Contreras D, González-Rocha A, Clark P, Barquera S, Denova-Gutiérrez E. Diagnostic accuracy of blood biomarkers and non-invasive scores for the diagnosis of NAFLD and NASH: Systematic review and meta-analysis. Ann Hepatol. 2023;28(1):100873. 10.1016/j.aohep.2022.100873
27 Yokoyama JS, Wang Y, Schork AJ, Thompson WK, Karch CM, Cruchaga C, et al. Alzheimer’s Disease Neuroimaging Initiative. Association between genetic traits for immune-mediated diseases and Alzheimer disease. JAMA Neurol. 2016;73(6):691–7. 10.1001/jamaneurol.2016.0150
28 Newcombe EA, Camats-Perna J, Silva ML, Valmas N, Huat TJ, Medeiros R. Inflammation: The link between comorbidities, genetics, and Alzheimer’s disease. J Neuroinflammation. 2018;15(1):276. 10.1186/s12974-018-1313-3
29 Karbalaei R, Allahyari M, Rezaei-Tavirani M, Asadzadeh-Aghdaei H, Zali MR. Protein-protein interaction analysis of Alzheimer’s disease and NAFLD based on systems biology methods unhide common ancestor pathways. Gastroenterol Hepatol Bed Bench. 2018;11(1):27–33.
30 Herman FJ, Simkovic S, Pasinetti GM. Neuroimmune nexus of depression and dementia: Shared mechanisms and therapeutic targets. Br J Pharmacol. 2019;176(18):3558–84. 10.1111/bph.14569
31 Terracciani F, Falcomatà A, Gallo P, Picardi A, Vespasiani-Gentilucci U. Prognostication in NAFLD: Physiological bases, clinical indicators, and newer biomarkers. J Physiol Biochem. 2023;79(4):851–68. 10.1007/s13105-022-00934-0
32 Nassir F. NAFLD: Mechanisms, treatments, and biomarkers. Biomolecules. 2022;12(6):824. 10.3390/biom12060824
33 Jie ZB, Ye NG; Xiao PT, Ma ZF, Jie ZW. Combining network pharmacology and bioinformatics to identify bioactive compounds and potential mechanisms of action of Sedum aizoon L in the treatment of atherosclerosis. Quality Assurance and Safety of Crops & Foods, 2023; 15(3): 104–116. 10.15586/qas.v15i3.1333
34 Wang H, Han X, Gao S. Identification of potential biomarkers for pathogenesis of Alzheimer’s disease. Hereditas. 2021;158(1):23. 10.1186/s41065-021-00187-9
35 Tomkins JE, Manzoni C. Advances in protein-protein interaction network analysis for Parkinson’s disease. Neurobiol Dis. 2021; 155:105395. 10.1016/j.nbd.2021.105395
36 Naveed M, Ali U, Aziz T, Jabeen K, Arif MH, Alharbi M, et al. Development and immunological evaluation of an mRNA-based vaccine targeting Naegleria fowleri for the treatment of primary amoebic meningoencephalitis. Sci Rep. 2024;14(1):767. 10.1038/s41598-023-51127-8
37 Naveed M, Jabeen K, Aziz T, Mughual MS, Ul-Hassan J, Sheraz M, et al. Whole proteome analysis of MDR Klebsiella pneumoniae to identify mRNA and multiple epitope based vaccine targets against emerging nosocomial and lungs associated infections. J Biomol Struct Dyn. 2025;43(4):1915–28. 10.1080/07391102.2023.2293266
38 Lv H, Liu X, Zeng X, Liu Y, Zhang C, Zhang Q, et al. Comprehensive analysis of cuproptosis-related genes in immune infiltration and prognosis in melanoma. Front Pharmacol. 2022; 13:930041. 10.3389/fphar.2022.930041
39 Naveed M, Mughal MS, Jabeen K, Aziz T, Naz S, Nazir N, et al. Evaluation of the whole proteome to design a novel mRNA-based vaccine against multidrug-resistant Serratia marcescens. Frontiers in microbiology. 2022;13, 960285. 10.3389/fmicb.2022.960285
40 Zhao J, Yang Z, Tu M, Meng W, Gao H, Li MD, et al. Correlation between prognostic biomarker SLC1A5 and immune infiltrates in various types of cancers including hepatocellular carcinoma. Front Oncol. 2021; 11:608641. 10.3389/fonc.2021.608641
41 Naveed M, Jabeen K, Naz R, Mughal MS, Rabaan AA, Bakhrebah MA, et al. Regulation of host immune response against Enterobacter cloacae proteins via computational mRNA vaccine design through transcriptional modification. Microorganisms. 2022;10(8):1621. 10.3390/microorganisms10081621
42 Guo C, Tang Y, Zhang Y, Li G. Mining TCGA data for key biomarkers related to immune microenvironment in endometrial cancer by immune score and weighted correlation network analysis. Front Mol Biosci. 2021; 8:645388. 10.3389/fmolb.2021.645388
43 Ye X, Liu M, Wang X, Cheng S, Li C, Bai Y, et al. Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis. Front Aging Neurosci. 2023; 15:1071391. 10.3389/fnagi.2023.1071391
2 Younossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C, Henry L. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): A systematic review. Hepatology. 2023;77(4):1335–47. 10.1097/hep.0000000000000004
3 Csernus EA, Werber T, Kamondi A, Horváth A. The significance of subclinical epileptiform activity in Alzheimer’s Disease: A review. Front Neurol. 2022; 13:856500. 10.3389/fneur.2022.856500
4 Yeh T, Kuo C, Chou Y. Retinal microvascular changes in mild cognitive impairment and Alzheimer’s disease: A systematic review, meta-analysis, and meta-regression. Front Aging Neurosci. 2022; 14:860759. 10.3389/fnagi.2022.860759
5 Alkhouri N, Almomani A, Le P. The prevalence of alcoholic and nonalcoholic fatty liver disease in adolescents and young adults in the United States: Analysis of the NHANES database. BMC Gastroenterol. 2022;22(1):366. 10.1186/s12876-022-02430-7
6 Le MH, Yeo YH, Zou B, Barnet S, Henry L, Cheung R, et al. Forecasted 2040 global prevalence of nonalcoholic fatty liver disease using hierarchical bayesian approach. Clin Mol Hepatol. 2022;28(4):841–50. 10.3350/cmh.2022.0239
7 Kelty T, Dashek R, Arnold W, Rector R. Emerging links between nonalcoholic fatty liver disease and neurodegeneration. Semin Liver Dis. 2023;43(1);77–88. 10.1055/s-0043-1762585
8 Li X, Wang V. Genetic evidence for the causal relationship between NAFLD and brain functional connectivity. Proceedings of the 6th International Conference on Biomedical Engineering and Applications. 2022. pp. 77–86. 10.1145/3543081.3543093
9 Gangopadhyay A, Ibrahim R, Theberge K, May M, Houseknecht K. Non-alcoholic fatty liver disease (NAFLD) and mental illness: Mechanisms linking mood, metabolism and medicines. Front Neurosci. 2022;16:1042442. 10.3389/fnins.2022.1042442
10 Zhu C, Huai Q, Zhang X, Dai H, Li X, Wang H. Insights into the roles and pathomechanisms of ceramide and sphigosine-1-phosphate in nonalcoholic fatty liver disease. Int J Biol Sci. 2023;19:311–30. 10.7150/ijbs.78525
11 Jiang L, Wang Q, Jiang Y, Peng D, Zong K, Li S, et al. Identification of diagnostic gene signatures and molecular mechanisms for non-alcoholic fatty liver disease and Alzheimer’s disease through machine learning algorithms. Clin Chim Acta. 2024; 557:117892. 10.1016/j.cca.2024.117892
12 Ye X, Liu M, Wang X, Cheng S, Li C, Bai Y, et al. Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis. Front Aging Neurosci. 2023; 15:1071391. 10.3389/fnagi.2023.1071391
13 Jadhav PA, Thomas AB, Chopada VM, Bokaria PV, Deokate SB, Chougule PS, et al. Correlation of non-alcoholic fatty liver disease and neurodegenerative disorders. Egyptian Liver J. 2024;14(1):79. 10.1186/s43066-024-00386-9
14 Hadjihambi A. Cerebrovascular alterations in NAFLD: Is it increasing our risk of Alzheimer’s disease? Anal Biochem. 2022; 636:114387. 10.1016/j.ab.2021.114387
15 Rita M, Giuffrè M, Crocè LS, Gazzin S, Tiribelli C. Nonalcoholic fatty liver disease and altered neuropsychological functions in patients with subcortical vascular dementia. J Pers Med. 2022;12(7):1106. 10.3390/jpm12071106
16 Bulgart HR, Neczypor EW, Wold LE, Mackos AR. Microbial involvement in Alzheimer disease development and progression. Mol Neurodegener. 2020;15(1):42. 10.1186/s13024-020-00378-4
17 Zhang JJ, Shen Y, Chen XY, Jiang ML, Yuan FH, Xie SL, et al. Integrative network-based analysis on multiple Gene Expression Omnibus datasets identifies novel immune molecular markers implicated in non-alcoholic steatohepatitis. Front Endocrinol. 2023; 14:1115890. 10.3389/fendo.2023.1115890.
18 Wolski WE, Nanni P, Grossmann J, d’Errico M, Schlapbach R, Panse C. prolfqua: A comprehensive R-package for proteomics differential expression analysis. J Proteome Res. 2023;22(4):1092–104. 10.1021/acs.jproteome.2c00441
19 Zhao K, Rhee SY. Interpreting omics data with pathway enrichment analysis. Trends Genet. 2023;39(4):308–19. 10.1016/j.tig.2023.01.003
20 Zhang X, Wang Z, Hu L, Shen X, Liu C. Identification of potential genetic biomarkers and target genes of peri-implantitis using bioinformatics tools. BioMed Res Int. 2021; 2021:1759214. 10.1155/2021/1759214
21 Xiang J, Huang W, He Y, Li Y, Wang Y, Chen R. Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis. Front Genet. 2022; 13:1041524. 10.3389/fgene.2022.1041524
22 Xu G, Qi F, Li H, Yang Q, Wang H, Wang X, et al. The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing. Cell Discov. 2020; 6:73. 10.1038/s41421-020-00225-2
23 Tang K, Li X, Mo J, Chen Y, Huang C, Li T, et al. CD69 serves as a potential diagnostic and prognostic biomarker for hepatocellular carcinoma. Sci Rep. 2023;13(1):7452. 10.1038/s41598-023-34261-1
24 Jiang H, Hu Y, Zhang Z, Chen X, Gao J. Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease. Lipids Health Dis. 2023;22(1):150. 10.1186/s12944-023-01911-2
25 Dan C, Jialiang L Taokan H Juan K Changhai J Jun L. Hepatic antioxidant and gut ecological modulation properties of long-term intake of tea (Camellia sinensis L.) flower extract in vivo. Quality Assurance and Safety of Crops & Foods, 2023; 15(3): 11–21. 10.15586/qas.v15i3.1209
26 Contreras D, González-Rocha A, Clark P, Barquera S, Denova-Gutiérrez E. Diagnostic accuracy of blood biomarkers and non-invasive scores for the diagnosis of NAFLD and NASH: Systematic review and meta-analysis. Ann Hepatol. 2023;28(1):100873. 10.1016/j.aohep.2022.100873
27 Yokoyama JS, Wang Y, Schork AJ, Thompson WK, Karch CM, Cruchaga C, et al. Alzheimer’s Disease Neuroimaging Initiative. Association between genetic traits for immune-mediated diseases and Alzheimer disease. JAMA Neurol. 2016;73(6):691–7. 10.1001/jamaneurol.2016.0150
28 Newcombe EA, Camats-Perna J, Silva ML, Valmas N, Huat TJ, Medeiros R. Inflammation: The link between comorbidities, genetics, and Alzheimer’s disease. J Neuroinflammation. 2018;15(1):276. 10.1186/s12974-018-1313-3
29 Karbalaei R, Allahyari M, Rezaei-Tavirani M, Asadzadeh-Aghdaei H, Zali MR. Protein-protein interaction analysis of Alzheimer’s disease and NAFLD based on systems biology methods unhide common ancestor pathways. Gastroenterol Hepatol Bed Bench. 2018;11(1):27–33.
30 Herman FJ, Simkovic S, Pasinetti GM. Neuroimmune nexus of depression and dementia: Shared mechanisms and therapeutic targets. Br J Pharmacol. 2019;176(18):3558–84. 10.1111/bph.14569
31 Terracciani F, Falcomatà A, Gallo P, Picardi A, Vespasiani-Gentilucci U. Prognostication in NAFLD: Physiological bases, clinical indicators, and newer biomarkers. J Physiol Biochem. 2023;79(4):851–68. 10.1007/s13105-022-00934-0
32 Nassir F. NAFLD: Mechanisms, treatments, and biomarkers. Biomolecules. 2022;12(6):824. 10.3390/biom12060824
33 Jie ZB, Ye NG; Xiao PT, Ma ZF, Jie ZW. Combining network pharmacology and bioinformatics to identify bioactive compounds and potential mechanisms of action of Sedum aizoon L in the treatment of atherosclerosis. Quality Assurance and Safety of Crops & Foods, 2023; 15(3): 104–116. 10.15586/qas.v15i3.1333
34 Wang H, Han X, Gao S. Identification of potential biomarkers for pathogenesis of Alzheimer’s disease. Hereditas. 2021;158(1):23. 10.1186/s41065-021-00187-9
35 Tomkins JE, Manzoni C. Advances in protein-protein interaction network analysis for Parkinson’s disease. Neurobiol Dis. 2021; 155:105395. 10.1016/j.nbd.2021.105395
36 Naveed M, Ali U, Aziz T, Jabeen K, Arif MH, Alharbi M, et al. Development and immunological evaluation of an mRNA-based vaccine targeting Naegleria fowleri for the treatment of primary amoebic meningoencephalitis. Sci Rep. 2024;14(1):767. 10.1038/s41598-023-51127-8
37 Naveed M, Jabeen K, Aziz T, Mughual MS, Ul-Hassan J, Sheraz M, et al. Whole proteome analysis of MDR Klebsiella pneumoniae to identify mRNA and multiple epitope based vaccine targets against emerging nosocomial and lungs associated infections. J Biomol Struct Dyn. 2025;43(4):1915–28. 10.1080/07391102.2023.2293266
38 Lv H, Liu X, Zeng X, Liu Y, Zhang C, Zhang Q, et al. Comprehensive analysis of cuproptosis-related genes in immune infiltration and prognosis in melanoma. Front Pharmacol. 2022; 13:930041. 10.3389/fphar.2022.930041
39 Naveed M, Mughal MS, Jabeen K, Aziz T, Naz S, Nazir N, et al. Evaluation of the whole proteome to design a novel mRNA-based vaccine against multidrug-resistant Serratia marcescens. Frontiers in microbiology. 2022;13, 960285. 10.3389/fmicb.2022.960285
40 Zhao J, Yang Z, Tu M, Meng W, Gao H, Li MD, et al. Correlation between prognostic biomarker SLC1A5 and immune infiltrates in various types of cancers including hepatocellular carcinoma. Front Oncol. 2021; 11:608641. 10.3389/fonc.2021.608641
41 Naveed M, Jabeen K, Naz R, Mughal MS, Rabaan AA, Bakhrebah MA, et al. Regulation of host immune response against Enterobacter cloacae proteins via computational mRNA vaccine design through transcriptional modification. Microorganisms. 2022;10(8):1621. 10.3390/microorganisms10081621
42 Guo C, Tang Y, Zhang Y, Li G. Mining TCGA data for key biomarkers related to immune microenvironment in endometrial cancer by immune score and weighted correlation network analysis. Front Mol Biosci. 2021; 8:645388. 10.3389/fmolb.2021.645388
43 Ye X, Liu M, Wang X, Cheng S, Li C, Bai Y, et al. Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis. Front Aging Neurosci. 2023; 15:1071391. 10.3389/fnagi.2023.1071391
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Nov 1, 2025
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Jabeen, K., Naveed, M., Aziz, T., Saad, M., Binsaleh, A. Y., Al-Hoshani, N., Abdulaziz Alkhateeb, M., Alhhazmi, A. A., & Mansouri, O. A. (2025). Identification of the common pathogenesis of Alzheimer’s and nonalcoholic fatty liver disease and exploration of their relationship with immune cells (M. S. Alwethaynani , Trans.). Allergologia Et Immunopathologia, 53(6), 20-31. https://doi.org/10.15586/aei.v53i6.1475
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How to Cite
Jabeen, K., Naveed, M., Aziz, T., Saad, M., Binsaleh, A. Y., Al-Hoshani, N., Abdulaziz Alkhateeb, M., Alhhazmi, A. A., & Mansouri, O. A. (2025). Identification of the common pathogenesis of Alzheimer’s and nonalcoholic fatty liver disease and exploration of their relationship with immune cells (M. S. Alwethaynani , Trans.). Allergologia Et Immunopathologia, 53(6), 20-31. https://doi.org/10.15586/aei.v53i6.1475