Circulating biomarkers of immunity and inflammation, risk of Alzheimer’s disease, and hippocampal volume: a Mendelian randomization study

Fani, Lana ; Georgakis, Marios; Ikram, M. Arfan ; Ikram, M. Kamran; Malik, Rainer und Dichgans, Martin (17. Mai 2021): Circulating biomarkers of immunity and inflammation, risk of Alzheimer’s disease, and hippocampal volume: a Mendelian randomization study. In: Translational Psychiatry, Bd. 11, 291

Abstract

The aim of this study was to explore the association between genetically predicted circulating levels of immunity and inflammation, and the risk of Alzheimer’s disease (AD) and hippocampal volume, by conducting a two-sample Mendelian Randomization Study. We identified 12 markers of immune cells and derived ratios (platelet count, eosinophil count, neutrophil count, basophil count, monocyte count, lymphocyte count, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, CD4 count, CD8 count, CD4-to-CD8 ratio, and CD56) and 5 signaling molecules (IL-6, fibrinogen, CRP, and Lp-PLA2 activity and mass) as primary exposures of interest. Other genetically available immune biomarkers with a weaker a priori link to AD were considered secondary exposures. Associations with AD were evaluated in The International Genomics of Alzheimer’s Project (IGAP) GWAS dataset (21,982 cases; 41,944 controls of European ancestry). For hippocampal volume, we extracted data from a GWAS meta-analysis on 33,536 participants of European ancestry. None of the primary or secondary exposures showed statistically significant associations with AD or with hippocampal volume following P-value correction for multiple comparisons using false discovery rate < 5% (Q-value < 0.05). CD4 count showed the strongest suggestive association with AD (odds ratio 1.32, P < 0.01, Q > 0.05). There was evidence for heterogeneity in the MR inverse variance-weighted meta-analyses as measured by Cochran Q, and weighted median and weighted mode for multiple exposures. Further cluster analyses did not reveal clusters of variants that could influence the risk factor in distinct ways. This study suggests that genetically predicted circulating biomarkers of immunity and inflammation are not associated with AD risk or hippocampal volume. Future studies should assess competing risk, explore in more depth the role of adaptive immunity in AD, in particular T cells and the CD4 subtype, and confirm these findings in other ethnicities.

• Dokumententyp: Zeitschriftenartikel
• EU Funded Grant Agreement Number: 667375
• EU-Projekte: Horizon 2020 > RIA - Research and Innovation action > SVDs-at-target - Small Vessel Diseases from a therapeutic perspective: Targets for intervention. Affected pathways and mechanistic exploitation for prevention of stroke dementia ; Horizon 2020 > RIA - Research and Innovation action > CoSTREAM - Common mechanisms and pathways in stroke and Alzheimer's disease
• Publikationsform: Publisher's Version
• Fakultät: Medizin > Institut für Schlaganfall- und Demenzforschung (ISD)
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• Bemerkung: This work was supported by the European Union’s Horizon 2020 research andinnovation program (grant numbers 667375 [“CoSTREAM”] and 666881[“SVDs@target”]) and Alzheimer Nederlan

Literaturliste

References Cao, W. & Zheng, H. Peripheral immune system in aging and Alzheimer’s disease. Mol. Neurodegener. 13, 51 (2018). - PubMed - PMC - DOI Heneka, M. T. et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol. 14, 388–405 (2015). - PubMed - PMC - DOI Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019). - PubMed - PMC - DOI Darweesh, S. K. L. et al. Inflammatory markers and the risk of dementia and Alzheimer’s disease: a meta-analysis. Alzheimers Dement. 14, 1450–1459 (2018). - PubMed - DOI Gate, D. et al. Clonally expanded CD8 T cells patrol the cerebrospinal fluid in Alzheimer’s disease. Nature 577, 399–404 (2020). - PubMed - PMC - DOI van der Willik, K. D. et al. Balance between innate versus adaptive immune system and the risk of dementia: a population-based cohort study. J. Neuroinflamm. 16, 68 (2019). - DOI Smith, G. D. & Ebrahim, S. Data dredging, bias, or confounding. BMJ 325, 1437–1438 (2002). - PubMed - PMC - DOI Smith, G. D. & Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003). - PubMed - DOI Prins, B. P. et al. Investigating the causal relationship of C-reactive protein with 32 complex somatic and psychiatric outcomes: a large-scale Cross-Consortium Mendelian Randomization Study. PLoS Med. 13, e1001976 (2016). - PubMed - PMC - DOI Larsson, S. C. et al. Modifiable pathways in Alzheimer’s disease: Mendelian randomisation analysis. BMJ 359, j5375 (2017). - PubMed - PMC - DOI Tsui, A. & Davis, D. Systemic inflammation and causal risk for Alzheimer’s dementia: Possibilities and limitations of a Mendelian randomization approach. Aging Med. (Milton) 1, 249–253 (2018). - DOI Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429 (2016). e1419. - PubMed - PMC - DOI Ligthart, S. et al. Genome analyses of >200,000 individuals identify 58 loci for chronic inflammation and highlight pathways that link inflammation and complex disorders. Am. J. Hum. Genet. 103, 691–706 (2018). - PubMed - PMC - DOI Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 8, 13624 (2017). - PubMed - PMC - DOI Foley, C. N., Kirk, P. D. W. & Burgess, S. MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates. Bioinformatics 37, 531–541 (2021). Lin, B. D. et al. 2SNP heritability and effects of genetic variants for neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio. J. Hum. Genet. 62, 979–988 (2017). - PubMed - PMC - DOI Georgakis, M. K. et al. Interleukin-6 signaling effects on ischemic stroke and other cardiovascular outcomes: a Mendelian randomization study. Circ. Genom. Precis. Med. 13, e002872 (2020). de Vries, P. S. et al. A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration. Hum. Mol. Genet. 25, 358–370 (2016). - PubMed - DOI - PMC Casas, J. P. et al. PLA2G7 genotype, lipoprotein-associated phospholipase A2 activity, and coronary heart disease risk in 10 494 cases and 15 624 controls of European Ancestry. Circulation 121, 2284–2293 (2010). - PubMed - PMC - DOI Burgess, S., Thompson, S. G. & Collaboration, C. C. G. Avoiding bias from weak instruments in Mendelian randomization studies. Int. J. Epidemiol. 40, 755–764 (2011). - PubMed - DOI Ahola-Olli, A. V. et al. Genome-wide association study identifies 27 loci influencing concentrations of circulating cytokines and growth factors. Am. J. Hum. Genet 100, 40–50 (2017). - PubMed - DOI Georgakis, M. K. et al. Genetically determined levels of circulating cytokines and risk of stroke. Circulation 139, 256–268 (2019). - PubMed - PMC - DOI Burgess, S. Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome. Int. J. Epidemiol. 43, 922–929 (2014). - PubMed - PMC - DOI Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013). - PubMed - PMC - DOI Greco, M. F., Minelli, C., Sheehan, N. A. & Thompson, J. R. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat. Med. 34, 2926–2940 (2015). - DOI Bowden, J., Davey, Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016). - PubMed - PMC - DOI Hartwig, F. P., Davey Smith, G. & Bowden, J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int. J. Epidemiol. 46, 1985–1998 (2017). - PubMed - PMC - DOI Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015). - PubMed - PMC - DOI Burgess, S., Foley, C. N., Allara, E., Staley, J. R. & Howson, J. M. M. A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat. Commun. 11, 376 (2020). - PubMed - PMC - DOI Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018). - PubMed - PMC - DOI Burgess, S., Butterworth, A. S. & Thompson, J. R. Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors. J. Clin. Epidemiol. 69, 208–216 (2016). - PubMed - PMC - DOI Kaul, M. HIV-1 associated dementia: update on pathological mechanisms and therapeutic approaches. Curr. Opin. Neurol. 22, 315–320 (2009). - PubMed - PMC - DOI Chitnis, T. The role of CD4 T cells in the pathogenesis of multiple sclerosis. Int. Rev. Neurobiol. 79, 43–72 (2007). - PubMed - PMC - DOI Schooling, C. M., Lopez, P., Yang, Z., Au Yeung, S. L. & Huang, J. V. Bias from competing risk before recruitment in Mendelian Randomization studies of conditions with shared etiology. Preprint at bioRxiv https://doi.org/10.1101/716621 (2019). Andrews, S. J., Fulton-Howard, B. & Goate, A. Interpretation of risk loci from genome-wide association studies of Alzheimer’s disease. Lancet Neurol. 19, 326–335 (2020). - PubMed - DOI - PMC Nelson, P. T. et al. Limbic-predominant age-related TDP-43 encephalopathy (LATE): consensus working group report. Brain 142, 1503–1527 (2019). - PubMed - PMC - DOI Burgess, S., Timpson, N. J., Ebrahim, S., Davey & Smith, G. Mendelian randomization: where are we now and where are we going? Int. J. Epidemiol. 44, 379–388 (2015). - PubMed - DOI - PMC Anand, A., Gupta, P. K., Sharma, N. K. & Prabhakar, S. Soluble VEGFR1 (sVEGFR1) as a novel marker of amyotrophic lateral sclerosis (ALS) in the North Indian ALS patients. Eur. J. Neurol. 19, 788–792 (2012). - PubMed - DOI Anand, A., Banik, A., Thakur, K. & Masters, C. L. The animal models of dementia and Alzheimer’s disease for pre-clinical testing and clinical translation. Curr. Alzheimer Res. 9, 1010–1029 (2012). - PubMed - DOI - PMC Goyal, K., Koul, V., Singh, Y. & Anand, A. Targeted drug delivery to central nervous system (CNS) for the treatment of neurodegenerative disorders: trends and advances. Cent. Nerv. Syst. Agents Med. Chem. 14, 43–59 (2014). - PubMed - DOI International Genomics of Alzheimer’s Disease Consortium Convergent genetic and expression data implicate immunity in Alzheimer’s disease. Alzheimers Dement. 11, 658–671 (2015). - DOI Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414 (2016). e1324. - PubMed - PMC - DOI Hu, Y. S., Xin, J., Hu, Y., Zhang, L. & Wang, J. Analyzing the genes related to Alzheimer’s disease via a network and pathway-based approach. Alzheimers Res. Ther. 9, 29 (2017). - PubMed - PMC - DOI Ridge, P. G. et al. Assessment of the genetic variance of late-onset Alzheimer’s disease. Neurobiol. Aging 41, e213–200 (2016). e220. - DOI Lin, B. D. et al. Heritability and GWAS studies for monocyte-lymphocyte ratio. Twin Res. Hum. Genet. 20, 97–107 (2017). - PubMed - DOI Ferreira, M. A. et al. Quantitative trait loci for CD4:CD8 lymphocyte ratio are associated with risk of type 1 diabetes and HIV-1 immune control. Am. J. Hum. Genet. 86, 88–92 (2010). - PubMed - PMC - DOI Ward-Caviness, C. K. et al. Mendelian randomization evaluation of causal effects of fibrinogen on incident coronary heart disease. PLoS ONE 14, e0216222 (2019). - PubMed - PMC - DOI Grallert, H. et al. Eight genetic loci associated with variation in lipoprotein-associated phospholipase A2 mass and activity and coronary heart disease: meta-analysis of genome-wide association studies from five community-based studies. Eur. Heart J. 33, 238–251 (2012). - PubMed - DOI Matteini, A. M. et al. Novel gene variants predict serum levels of the cytokines IL-18 and IL-1ra in older adults. Cytokine 65, 10–16 (2014). - PubMed - DOI Interleukin 1 Genetics Consortium Cardiometabolic effects of genetic upregulation of the interleukin 1 receptor antagonist: a Mendelian randomisation analysis. Lancet Diabetes Endocrinol. 3, 243–253 (2015). - DOI Barbalic, M. et al. Large-scale genomic studies reveal central role of ABO in sP-selectin and sICAM-1 levels. Hum. Mol. Genet. 19, 1863–1872 (2010). - PubMed - PMC - DOI Pare, G. et al. Genome-wide association analysis of soluble ICAM-1 concentration reveals novel associations at the NFKBIK, PNPLA3, RELA, and SH2B3 loci. PLoS Genet. 7, e1001374 (2011). - PubMed - PMC - DOI Kunkle, B. W. et al. Author Correction: Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing. Nat. Genet. 51, 1423–1424 (2019). - PubMed - PMC - DOI