Personalised medicine, genetics and Big Data: the “New Jerusalem” for dementia?

The fact that there are real individuals at the heart of a policy strand summarised as ‘young onset dementia’ is all too easily forgotten, especially by people who prefer to construct “policy by spreadsheet”.

It is relatively uncommon for a dementia to be down to a single gene, but it can happen. And certainly, even if there might not be ‘cure’ for today or tomorrow, identification of precise genetic abnormalities might provide scope for genetic counseling. Markus (2012) argues that many monogenic forms of stroke are untreatable, and therefore, specialised genetic counseling is important before mutation testing. This could be particularly important in asymptomatic individuals, or those with mild disease; for example, potential cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) patients who have migraine but have not yet developed stroke or dementia. Mackenzie and colleagues (Mackenzie et al., 2006) published on a group of families with a clinical diagnosis of tau-negative, ubiquitin-immunoreactive neuronal inclusions (NII). The authors discussed how findings across the literature appeared to suggest that, in this particular condition, NII are a highly sensitive pathological marker for progranulin genetic mutations and their demonstration may be a way of identifying cases and families that should undergo genetic screening.

But is this genomics revolution the beginning of a “New Jerusalem” in dementia, beyond the headlines?

“Big data” refers to information that is too large, varied, or high-speed for traditional methods of storage, processing, and analytics. For example, one application of mining large datasets that has been particularly productive in the research community is the search for genome-wide associations (“Genome-Wide Association Studies (“GWAS”)). GWAS rely on analysis of DNA segments across vast patient populations to search for DNA variants associated with a particular disease. To date, GWAS analyses have identified a handful of promising genetic associations with Alzheimer’s disease, including Apo E4.

This is clearly wonderful if “money does grow on trees”, but the concern for initiatives such as these such work is resource-intensive, and diverts resources from frontline improvements in wellbeing of people living with dementia. Investors also have to be mindful of their financial return compared to the risk of such initiatives. One of the biggest complaints of proponents of “Big Data” is that data tend to be pocketed in a fragmented, piecemeal fashion.

As the McKinsey Centre for Business Technology (2012) state in an interesting document called, “Perspectives on digital business”:

 “The US health care sector is dotted by many small companies and individual physicians’ practices. Large hospital chains, national insurers, and drug manufactuers, by contrast, stand to gain substantially through the pooling and more effective analysis of data.”

Vast collections of genomic data obviously represent a goldmine for health providers around the world. Meltzer (2013) reviews correctly that personalized medicine been the subject of increased basic and clinical research interest and funding. Meltzer describes that a knowledge of the genetic and molecular basis of clinical heterogeneity should make it possible to more reliably predict the likely outcomes of alternative approaches to treatment for specific individuals and therefore what course of action is likely to be best for any given patient. Knowledge of personal genetic traits might allow accurate prediction of those invididuals who are most likely to experience adverse events through medication (Markus, 2012).

Both ‘Big Data’ and ‘personalised medicine’, in being couched language of bringing value to operational processes in corporate strategy, tend to lose the precise cost-effectiveness arguments at an accounting level. The new CEO of NHS England, Simon Stevens, will have raised eyebrows with the Guardian piece entitled, “New NHS boss: service must become world leader in personalised medicine” from 4 June 2014 in “The Guardian” newspaper (Campbell, 2014) . Whether the National Health Service of the UK can cope with this, with inevitable transfer of funds from the public funds to private funds, with all the talk of ‘sustainability’, is a different matter. It is difficult to predict what the uptake of personalised medicines will be, even if every patient has access to his or her personal genomic sequence in years to come. All jurisdictions have to consider whether they can justify the sharing of information for public interest overcoming concerns about data privacy and security, and ultimately this is a question of legal proportionality.

The pitch from corporate investors tend to minimise biological practicalities too. For example, it is still yet to be determined what the precise interplay between genetic and environmental factors are, particularly for the young onset dementias. And the assumption that all ‘big’ data are ‘good’ data could be a fallacy. There are 1000 billion neurones in the human brain, and it is well known that not all neuronal connections between them are ‘productive’; in fact a sizeable number are redundant. Heterogeneity in genetic sequences might be meaningful, or utterly spurious, and it could be a costly experiment to wait to find out how, when there are more pressing considerations about both care and cure.

But is this genomics revolution the beginning of a “New Jerusalem” in dementia, beyond the headlines?

Frontotemporal lobar degeneration (FTLD) is the second most common cause of dementia in individuals younger than 65 years (Ratnavilli et al., 2002). It is a progressive neurodegenerative disorder characteristically defined by behavioural changes, executive dysfunction and language deficits. The behavioural variant of FTLD is characterised in its earliest stages by a progressive, insidious change in behaviour and personality, considered to reflect underlying problems in the ventromedial prefrontal cortex (Rahman et al., 1999). FTLD has a strong genetic background, as supported by positive family history in up to 40% of cases, higher than what reported in other neurodegenerative disorders and by the identification of causative genes related to the disease (Seelaar et al., 2011). The notion that genetic background might affect disease outcomes and rate of survival, modulating the onset and the progression of the pathological process when disease is overt (Premi et al., 2012). Given the consolidated role of genetic loading in FTLD, the likely effect of environment has almost been neglected.

Only recently, it has been reported that modifiable factors, i.e. education and occupation, might act as proxies for reserve capacity in FTLD. Patients with a high level of education and occupation can recruit an alternative neural network to cope better with cognitive functions (e.g. Borroni et al., 2009; Spreng et al., 2011). But the search for treatments for particular types of dementia based on their underlying genes and genetic products is arguably not an unreasonable one. A good example is provided by the Horizon Scanning Centre of the National Institute for Health Research of NHS England in September 2013 (NIHR HSC ID: 8239): leuco-methylthioninium, which is a “tau protein aggregation inhibitor”. It acts by preventing the formation and spread of neurofibrillary tangles, which consist of aberrant tau protein clusters that aggregate within neurons causing toxicity and neuronal cell death in the brain of patients with certain forms of dementia. Leuco-methylthioninium is a stabilised, reduced form of charged methylthioninium chloride. The clinical trials for this are under way. The medication at the time of writing may or may not work safely.

No. This genomics revolution the beginning of a “New Jerusalem” in dementia, especially when social care is on its knees.



Borroni B, Premi E, Agosti C, Alberici A, Garibotto V, Bellelli G, Paghera B, Lucchini S, Giubbini R, Perani D, Padovani A. (2009) Revisiting brain reserve hypothesis in frontotemporal dementia: evidence from a brain perfusion study. Dement Geriatr Cogn Disord, 28, pp. 130–135

Campbell, D. (2014) New NHS boss: service must become world leader in personalised medicine, The Guardian, 4 June.

Mackenzie, I.R., Baker, M., Pickering-Brown, S., Hsiung, G.Y., Lindholm, C., Dwosh, E., Gass, J., Cannon, A., Rademakers, R., Hutton, M., Feldman, H.H. (2006) The neuropathology of frontotemporal lobar degeneration caused by mutations in the progranulin gene, Brain, 129(Pt 11), pp. 3081-90.

Mendez, M. (2006) The accurate diagnosis of early-onset dementia. Int J Psychiatry Med, 36(4), pp. 401– 12.

McKinsey Centre for Business Technology (2012) Perspectives on digital business.

Rahman, S., Sahakian, B.J., Hodges, J.R., Rogers, R.D., Robbins, T.W. (1999) Specific cognitive deficits in mild frontal variant frontotemporal dementia, 122 (Pt 8), pp. 1469-93.

Ratnavalli E, Brayne C, Dawson K, Hodges JR. (2002) The prevalence of frontotemporal dementia. Neurology, 58(11), pp. 1615-1621.

Spreng, R.N., Drzezga, A., Diehl-Schmid, J., Kurz, A., Levine, B., Perneczky, R. (2011) Relationship between occupation attributes and brain metabolism in frontotemporal dementia,  Neuropsychologia, 49, pp. 3699–3703.

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