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Common Aging Signature in the Peripheral Blood of Vascular Dementia and Alzheimer’s Disease

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Abstract

Alzheimer’s disease (AD) and vascular dementia (VaD) are the two most dominant forms of dementia in elderly people. Due to the large overlap between AD and VaD in clinical observations, great controversies exist regarding the distinction and connection between these two types of senile dementia. Here for the first time, we resort to the gene expression pattern of the peripheral blood to compare AD and VaD objectively. In our previous work, we have demonstrated that the dysregulation of gene expression in AD is unique among the examined diseases including neurological diseases, cancer, and metabolic diseases. In this study, we found that the dysregulation of gene expression in AD and VaD is quite similar to each other at both functional and gene levels. Interestingly, the dysregulation started at the early stages of the diseases, namely mild cognitive impairment (MCI) and vascular cognitive impairment (VCI). We have also shown that this signature is distinctive from that of peripheral vascular diseases. Comparison with aging studies revealed that the most profound change in AD and VaD, namely ribosome, is consistent with the accelerated aging scenario. This study may have implications to the common mechanism between AD and VaD.

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References

  1. Kling MA et al (2013) Vascular disease and dementias: paradigm shifts to drive research in new directions. Alzheimers Dement 9(1):76–92

    Article  PubMed  Google Scholar 

  2. Korczyn AD, Vakhapova V, Grinberg LT (2012) Vascular dementia. J Neurol Sci 322(1–2):2–10

    Article  PubMed  PubMed Central  Google Scholar 

  3. Attems J, Jellinger KA (2014) The overlap between vascular disease and Alzheimer’s disease—lessons from pathology. BMC Med 12:206

    Article  PubMed  PubMed Central  Google Scholar 

  4. Mathias JL, Burke J (2009) Cognitive functioning in Alzheimer’s and vascular dementia: a meta-analysis. Neuropsychology 23(4):411–23

    Article  CAS  PubMed  Google Scholar 

  5. Grinberg LT, Heinsen H (2010) Toward a pathological definition of vascular dementia. J Neurol Sci 299(1–2):136–8

    Article  PubMed  PubMed Central  Google Scholar 

  6. Simonsen AH et al (2012) Protein markers for the differential diagnosis of vascular dementia and Alzheimer’s disease. Int J Proteomics 2012:824024

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Gussago C et al (2014) Different adenosine A2A receptor expression in peripheral cells from elderly patients with vascular dementia and Alzheimer’s disease. J Alzheimers Dis 40(1):45–9

    CAS  PubMed  Google Scholar 

  8. Han G et al (2013) Characteristic transformation of blood transcriptome in Alzheimer’s disease. J Alzheimers Dis 35(2):373–86

    CAS  PubMed  Google Scholar 

  9. Lunnon K et al (2013) A blood gene expression marker of early Alzheimer’s disease. J Alzheimers Dis 33(3):737–53

    CAS  PubMed  Google Scholar 

  10. Roed L et al (2013) Prediction of mild cognitive impairment that evolves into Alzheimer’s disease dementia within two years using a gene expression signature in blood: a pilot study. J Alzheimers Dis 35(3):611–21

    CAS  PubMed  Google Scholar 

  11. Huang CC et al (2011) Gene expression variation between African Americans and whites is associated with coronary artery calcification: the multiethnic study of atherosclerosis. Physiol Genomics 43(13):836–43

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Elashoff MR et al (2011) Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients. BMC Med Genomics 4:26

    Article  PubMed  PubMed Central  Google Scholar 

  13. Beineke P et al (2012) A whole blood gene expression-based signature for smoking status. BMC Med Genomics 5:58

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sinnaeve PR et al (2009) Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease. PLoS ONE 4(9):e7037

    Article  PubMed  PubMed Central  Google Scholar 

  15. Mosig S et al (2008) Monocytes of patients with familial hypercholesterolemia show alterations in cholesterol metabolism. BMC Med Genomics 1:60

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lewis DA et al (2011) Whole blood gene expression analyses in patients with single versus recurrent venous thromboembolism. Thromb Res 128(6):536–40

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Krug T et al (2012) TTC7B emerges as a novel risk factor for ischemic stroke through the convergence of several genome-wide approaches. J Cereb Blood Flow Metab 32(6):1061–72

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Li J et al (2011) Vascular risk factors promote conversion from mild cognitive impairment to Alzheimer disease. Neurology 76(17):1485–91

    Article  CAS  PubMed  Google Scholar 

  19. Katzman R et al (1988) A Chinese version of the mini-mental state examination; impact of illiteracy in a shanghai dementia survey. J Clin Epidemiol 41(10):971–8

    Article  CAS  PubMed  Google Scholar 

  20. Minemawari Y, Kato T, Aso K (2000) Cognitive function and basic activity of daily living of elderly disabled inpatients. Nihon Ronen Igakkai Zasshi 37(3):225–32

    Article  CAS  PubMed  Google Scholar 

  21. O’Bryant SE et al (2010) Validation of the new interpretive guidelines for the clinical dementia rating scale sum of boxes score in the national Alzheimer’s coordinating center database. Arch Neurol 67(6):746–9

    PubMed  PubMed Central  Google Scholar 

  22. Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23:56–62

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hachinski V et al (2006) National Institute of Neurological Disorders and Stroke-Canadian Stroke Network vascular cognitive impairment harmonization standards. Stroke 37(9):2220–41

    Article  PubMed  Google Scholar 

  24. Roman GC et al (1993) Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 43(2):250–60

    Article  CAS  PubMed  Google Scholar 

  25. Du P, Kibbe WA, Lin SM (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24(13):1547–8

    Article  CAS  PubMed  Google Scholar 

  26. Hastie T et al (2001) Impute: imputation for microarray data. Bioinformatics 17(6):520–525

    Article  PubMed  Google Scholar 

  27. Leek JT et al (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28(6):882–3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hong F et al (2006) RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis. Bioinformatics 22(22):2825–7

    Article  CAS  PubMed  Google Scholar 

  29. Borsatto B, Smith M (1996) Reduction of the activity of ribosomal genes with age in Down’s syndrome. Gerontology 42(3):147–154

    Article  CAS  PubMed  Google Scholar 

  30. da Silva AMÁ et al (2000) Quantitative evaluation of the rRNA in Alzheimer’s disease. Mech Ageing Dev 120(1):57–64

    Article  PubMed  Google Scholar 

  31. Bosetti F et al (2002) Cytochrome c oxidase and mitochondrial F 1 F 0-ATPase (ATP synthase) activities in platelets and brain from patients with Alzheimer’s disease. Neurobiol Aging 23(3):371–376

    Article  CAS  PubMed  Google Scholar 

  32. Hara M et al (2013) S100A12 gene expression is increased in peripheral leukocytes in chronic kidney disease stage 4–5 patients with cardiovascular disease. Nephron Clin Pract 123(3–4):202–8

    Article  CAS  PubMed  Google Scholar 

  33. Liu J et al (2014) Serum S100A12 concentrations are correlated with angiographic coronary lesion complexity in patients with coronary artery disease. Scand J Clin Lab Invest 74(2):149–54

    Article  CAS  PubMed  Google Scholar 

  34. Saito T et al (2012) S100A12 as a marker to predict cardiovascular events in patients with chronic coronary artery disease. Circ J 76(11):2647–2652

    Article  CAS  PubMed  Google Scholar 

  35. Shiotsu Y et al (2011) Plasma S100A12 level is associated with cardiovascular disease in hemodialysis patients. Clin J Am Soc Nephrol 6(4):718–723

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhao P et al (2013) Serum S100A12 levels are correlated with the presence and severity of coronary artery disease in patients with type 2 diabetes mellitus. J Investig Med 61(5):861–6

    Article  CAS  PubMed  Google Scholar 

  37. Hochmeister S et al (2006) Dysferlin is a new marker for leaky brain blood vessels in multiple sclerosis. J Neuropathol Exp Neurol 65(9):855–865

    Article  CAS  PubMed  Google Scholar 

  38. Fehlbaum-Beurdeley P et al (2010) Toward an Alzheimer’s disease diagnosis via high-resolution blood gene expression. Alzheimers Dement 6(1):25–38

    Article  CAS  PubMed  Google Scholar 

  39. Lunnon K et al (2012) Mitochondrial dysfunction and immune activation are detectable in early Alzheimer’s disease blood. J Alzheimers Dis 30(3):685–710

    CAS  PubMed  Google Scholar 

  40. Rye PD et al (2011) A novel blood test for the early detection of Alzheimer’s disease. J Alzheimers Dis 23(1):121–9

    CAS  PubMed  Google Scholar 

  41. Scherzer CR et al (2007) Molecular markers of early Parkinson’s disease based on gene expression in blood. Proc Natl Acad Sci U S A 104(3):955–60

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bai Z et al (2015) AlzBase: an integrative database for gene dysregulation in Alzheimer’s disease. Mol Neurobiol. doi:10.1007/s12035-014-9011-3

    Google Scholar 

  43. Ross GW et al (1999) Characterization of risk factors for vascular dementia: the Honolulu-Asia Aging Study. Neurology 53(2):337–43

    Article  CAS  PubMed  Google Scholar 

  44. Kalaria R (2002) Similarities between Alzheimer’s disease and vascular dementia. J Neurol Sci 203–204:29–34

    Article  PubMed  Google Scholar 

  45. Harries LW et al (2011) Human aging is characterized by focused changes in gene expression and deregulation of alternative splicing. Aging Cell 10(5):868–878

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. van den Akker EB et al (2014) Meta-analysis on blood transcriptomic studies identifies consistently coexpressed protein-protein interaction modules as robust markers of human aging. Aging cell 13(2):216–25

    Article  PubMed  Google Scholar 

  47. Inouye M et al (2010) An immune response network associated with blood lipid levels. PLoS Genet 6(9):e1001113

    Article  PubMed  PubMed Central  Google Scholar 

  48. Holly AC et al (2013) Changes in splicing factor expression are associated with advancing age in man. Mech Ageing Dev 134(9):356–66

    Article  CAS  PubMed  Google Scholar 

  49. Sun J et al (2012) Down-regulation of energy metabolism in Alzheimer’s disease is a protective response of neurons to the microenvironment. J Alzheimers Dis 28(2):389–402

    CAS  PubMed  Google Scholar 

  50. Whitney AR et al (2003) Individuality and variation in gene expression patterns in human blood. Proc Natl Acad Sci 100(4):1896–1901

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. De Jong S et al (2014) Seasonal changes in gene expression represent cell-type composition in whole blood. Hum Mol Genet 23(10):2721–2728

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

We are very grateful for all the participants involved in this project. This work was supported by the grant from the National High Technology Program of China (863 Program; Grant No. 2015AA020108) and the National Basic Research Program of China (973 Program; Grant No. 2014CB964901) awarded to H Lei by the Ministry of Science and Technology of China. This work was also supported by the Natural science foundation of China (Grant 81303097) and the Natural science foundation of Gansu province (Grant 1308RJZA304) awarded to H Luo.

Conflict of Interest

The authors declare that there are no conflicts of interest.

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    Authors and Affiliations

    1. Department of Neurology, Lanzhou General Hospital, Lanzhou military Area Command, Lanzhou, Gansu, 730050, China

      Hongbo Luo, Yuanming Li, Shaoju Shao & Xiangqun Shi

    2. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China

      Guangchun Han, Jiajia Wang, Fuhai Song, Zhouxian Bai, Xing Peng & Hongxing Lei

    3. University of Chinese Academy of Sciences, Beijing, 100049, China

      Jiajia Wang, Fuhai Song, Zhouxian Bai & Xing Peng

    4. Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 100053, China

      Fan Zeng & Yan-Jiang Wang

    5. Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China

      Hongxing Lei

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    1. Hongbo Luo
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    Correspondence to Xiangqun Shi or Hongxing Lei.

    Additional information

    Hongbo Luo and Guangchun Han contributed equally to this work.

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    Luo, H., Han, G., Wang, J. et al. Common Aging Signature in the Peripheral Blood of Vascular Dementia and Alzheimer’s Disease. Mol Neurobiol 53, 3596–3605 (2016). https://doi.org/10.1007/s12035-015-9288-x

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