Blood biomarkers for neurological disease research: current status, challenge and future outlook

Written by Liu Shi (Department of Psychiatry, University of Oxford, UK)

Biomarker discovery for neurological diseases is burgeoning due to the advances in technologies that permit molecular measures taken from the brain, cerebrospinal fluid (CSF), plasma, saliva, urine and so on. Biomarkers could be biochemical changes (proteins, metabolomics and lipids), genetic alteration or changes in structural or functional features. They could help the diagnosis and detect the progression of these diseases, referred to as diagnostic and prognostic markers, respectively. Furthermore, biomarkers could help to measure the efficacy of the treatments, known as predictive markers.
The importance of biomarkers in neurological diseases should not be underestimated, particularly considering the large social and economic burden presently attributed to these diseases. This article describes the current status of blood-based biomarker research in neurological disorders, particularly in Alzheimer’s disease (AD), as well as addresses the main challenges and future direction of this field.
Current status of blood biomarker development in neurological disease

CSF is one of the main resources for biomarker development for neurological diseases given that CSF surrounds the brain and spinal cord. For example, the levels of amyloid and tau in CSF have been used in diagnosing AD [1]. However, these measures are challenging because of invasiveness, cost and limited availability [2,3]. In part due to these limitations, increasing numbers of studies have attempted to find biomarkers in blood; a tissue that is easily accessible and suitable for repeated measures throughout the disease course or over the time-frame of an interventional study. Previous reviews have summarized much of this growing research effort to find biomarkers for AD diagnosis [4–11], as well as for other neurological disease [12–16]. Though it looks promising, it is important to note that there is no blood-based biomarker used in clinic for neurological disease diagnosis yet, mainly due to the lack of replication.

Challenge: why do most blood biomarkers fail replication?

Although a number of plasma biomarkers of diagnosis, disease severity and progression have been identified, a key concern for the field has been the lack of reproducibility of these results.”

Although a number of plasma biomarkers of diagnosis, disease severity and progression have been identified, a key concern for the field has been the lack of reproducibility of these results. The reason for such non-reproducibility might be caused by the heterogeneity of the disease itself as well as the complexity of blood. Furthermore, a number of other major factors could also lead to the failure of replication. They include pre-analytical sample handling, analysis of different blood fractions, use of different analytical platforms and inappropriate statistical analysis [17]. For example, Huebinger et al. compared the concentration of 100 proteins in matched samples of serum and plasma from 39 AD patients. They found that only 40 proteins were highly correlated between blood fractions while the remaining proteins were only moderately or weakly correlated, including some of considerable interest in AD [18].

Solutions: better study design, standardization and multimodal biomarkers

To address the challenges of blood-based biomarker development, several aspects should be taken into consideration. First is the study design. Most biomarker studies use a case-control study design, namely the cases included in this design had established disease. Diagnosis of established neurological disease is not difficult, whereas diagnosing the disease in the very early stages is challenging. In contrast to case-control approaches, phenotype-based approaches are more reliable. The latter approach aims to find biomarkers related to early pathology of neurological disease. Let’s take AD for an example: one of the key early pathologies of AD is amyloid deposition in brain. Therefore, finding blood biomarkers relating to brain amyloid deposition could help the early diagnosis of AD.

It is now critical to set standards for validation in the field so that promising biomarkers can be applied in clinical trials and clinical practice.

Second is the standardization of measurement of these biomarkers. Since all these factors could influence the results, at the very least such parameters should be recorded, and standardization of methodologies would be desirable. Currently, an international working group led by O’Bryant has provided the initial starting point for such guidelines and standardized operating procedures [19]. It is only through cooperation and collaboration of all academic societies that we will make progress.

Last but not the least is the usage of multimodel biomarkers. The complexity and the long prodromal phase of neurological disease demands the combination of different kinds of biomarkers together including metabolite or lipidomic markers, genomics including miRNAs and epigenetic changes, transcriptomics and other markers. The challenges of data management and analysis of any one of these approaches will be considerable but the real value might emerge when combinatorial analysis becomes possible.

In conclusion, integrated, collaborative efforts are needed to standardize a multimodal set of biomarkers with dynamic range, optimize the methods, and conduct sufficiently powered, multi-site studies so that these tools progress rapidly to clinical qualification by regulatory agencies. It is now critical to set standards for validation in the field so that promising biomarkers can be applied in clinical trials and clinical practice.

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  1. McKhann GM, Knopman DS, Chertkow H et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7(3), 263–269 (2011).
  2. de Almeida SM, Shumaker SD, LeBlanc SK et al. Incidence of post-dural puncture headache in research volunteers. Headache 51(10), 1503–1510 (2011).
  3. Lista S, Faltraco F, Prvulovic D, Hampel H. Blood and plasma-based proteomic biomarker research in Alzheimer’s disease. Prog. Neurobiol. 101–102, 1–17 (2013).
  4. Olsson B, Lautner R, Andreasson U et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Lancet Neurol. 15(7), 673–684 (2016).
  5. Khan AT, Dobson RJB, Sattlecker M, Kiddle SJ. Alzheimer’s disease: are blood and brain markers related? A systematic review. Ann. Clin. Transl. Neurol. 3(6), 455–462 (2016).
  6. Carmona P, Molina M, Toledano A. Blood-based biomarkers of Alzheimer’s disease: diagnostic algorithms and new technologies. Curr. Alzheimer Res. 13(4), 450–464 (2016).
  7. Baird AL, Westwood S, Lovestone S. Blood-based proteomic biomarkers of Alzheimer’s disease pathology. Front. Neurol. 6, 236 (2015).
  8. Sutphen CL, Fagan AM, Holtzman DM. Progress update: fluid and imaging biomarkers in Alzheimer’s disease. Biol. Psychiatry 75(7), 520–-526 (2014).
  9. Snyder HM, Carrillo MC, Grodstein F et al. Developing novel blood-based biomarkers for Alzheimer’s disease. Alzheimers Dement. 10(1), 109–114 (2014).
  10. Henriksen K, O’Bryant SE, Hampel H et al. The future of blood-based biomarkers for Alzheimer’s disease. Alzheimers Dement. 10(1), 115–131 (2014).
  11. Thambisetty M, Lovestone S. Blood-based biomarkers of Alzheimer’s disease: challenging but feasible. Biomark. Med. 4(1), 65–79 (2010).
  12. Singh S, Gupta SK, Seth PK. Biomarkers for detection, prognosis and therapeutic assessment of neurological disorders. Rev. Neurosci. 29(7), 771–789 (2018).
  13. Miller DB O’Callaghan JP. Biomarkers of Parkinson’s disease: present and future. Metabolism 64(3 Suppl 1), S40–46 (2015).
  14. Jeromin A Bowser R. Biomarkers in neurodegenerative diseases. Adv. Neurobiol. 15, 491–528 (2017).
  15. Nayak A, Salt G, Verma SK, Kishore U. Proteomics approach to identify biomarkers in neurodegenerative diseases. Int. Rev. Neurobiol. 121, 59–86 (2015).
  16. Botas A, Campbell HM, Han X, Maletic-Savatic M. Metabolomics of neurodegenerative diseases. Int. Rev. Neurobiol. 122, 53–80 (2015).
  17. Shi L, Baird AL, Westwood S et al. A decade of blood biomarkers for Alzheimer’s disease research: an evolving field, improving study designs, and the challenge of replication. J. Alzheimers Dis. 62(3), 1181–1198 (2018).
  18. Huebinger RM, Xiao G, Wilhelmsen KC et al. Comparison of protein concentrations in serum versus plasma from Alzheimer’s patients. Advances in Alzheimer’s Disease 1(3), 51 (2012).
  19. O’Bryant SE, Gupta V, Henriksen K et al. Guidelines for the standardization of preanalytic variables for blood-based biomarker studies in Alzheimer’s disease research. Alzheimers Dement. 11(5), 549–560 (2015).