Research Roundup

Amyotrophic Lateral Sclerosis with Frontotemporal Dementia

Contributor: Daniella Ladowski, M.Sc., University of Western Ontario (Doctoral Candidate)

Posted: 5/15/18

Topic overview: Amyotrophic Lateral Sclerosis (ALS), also referred to as motor neuron disease, is a progressive disease affecting both upper and lower motor neurons. According to the El Escorial criteria [1] and its revisions [2-5], diagnosis is largely based on clinical and electrophysiological evidence of motor neuron dysfunction to the exclusion of other motor disorders. However, ALS may be conceptualized as a collection of disorders with numerous phenotypic variants, each with its own implications for prognosis and treatment [6-7]. One such variant is ALS with comorbid frontotemporal dementia (FTD).

One third to one half of individuals with ALS exhibit cognitive impairment in at least one domain, and approximately 15% meet criteria for FTD [8-9]. It is unlikely that ALS with FTD represents a discrete diagnostic category; rather, cognitive and behavioural symptoms that are characteristic of FTD appear to fall on a continuum in ALS [6]. Among individuals with ALS, common cognitive symptoms include executive dysfunction (e.g., impaired verbal fluency, complex attention, cognitive flexibility) and to a lesser degree, language and memory impairment [10-12]. Behavioural abnormalities may also be prominent, including apathy, irritability, disinhibition, and impaired social cognition [12-14]. Based on the observation that individuals without dementia may exhibit clinically meaningful cognitive and behavioural changes, Strong and colleagues [15] proposed a classification system for frontotemporal syndromes in ALS, which characterizes syndromes that meet criteria for a dementia diagnosis [ALS-FTD and ALS-comorbid dementia (non-FTD)] as well as those that do not meet full criteria but represent significant impairments (ALS-behavioural impairment and ALS-cognitive impairment).

In addition to cognitive and behavioural symptomatology, ALS and FTD share common neuropathological features. In neuroimaging studies, cognitively-impaired individuals with ALS exhibit patterns of atrophy and hypometabolism/hypoperfusion in frontotemporal regions that are comparable to those observed in FTD [13,16-18]. Even in the absence of overt dementia, large-scale abnormalities in cortical thickness and functional connectivity have been detected in ALS [19-20]. In terms of pathogenesis, some genetic mutations and other biomarkers have been implicated in both ALS and FTD [12]. For example, in 2006, Neumann and colleagues [21] discovered that TAR DNA-binding protein 43 (TDP-43) was the major disease protein in both ALS and FTD. In 2011, mutations of the C9ORF72 gene were shown to explain familial cases of co-occurring ALS and FTD linked to chromosome 9 [22-23]. These and other discoveries are further evidence of the disease continuum that accounts for ALS and FTD concordance.

Detection of cognitive impairment in individuals with ALS is essential for treatment planning. Individuals with ALS-FTD demonstrate lower rates of compliance with nutritional and respiratory interventions compared to individuals with ALS alone [24]. Since these interventions are administered in advanced stages of the disease to prolong survival, noncompliance likely contributes to the shorter survival times observed among individuals with ALS-FTD compared to ALS alone [24-25]. Among individuals without FTD, executive impairment may also predict shorter survival in ALS, whereas more subtle or non-executive impairments are not thought to affect survival [25-26]. Greater cognitive impairment in ALS has also been linked to increased caregiver burden [27]. Finally, capacity in healthcare decision-making is an important consideration in ALS, especially as it relates to end-of-life decisions. Given that cognitive symptoms often precede motor symptoms in ALS-FTD [26], it may be prudent to establish advance directives at the earliest signs of cognitive decline before decision-making becomes affected and speech/motor disturbances hinder communication. In light of these challenges, early neuropsychological assessment should be undertaken in order to best prepare patients, caregivers, and clinicians for the road ahead.

Highlighted abstract:
Beeldman, E., Raaphorst, J., Klein Twennaar, M., Govaarts, R., Pijnenburg, Y. A. L., de Haan, R. J., … Schmand, B. A. (2018). The cognitive profile of behavioural variant FTD and its similarities with ALS: A systematic review and meta-analysis. Journal of Neurology, Neurosurgery & Psychiatry. Advance online publication. http://doi.org/10.1136/jnnp-2017-317459
https://www.ncbi.nlm.nih.gov/pubmed/29439163

Abstract: Approximately 30% of patients with amyotrophic lateral sclerosis (ALS) have cognitive impairment and 8%–14% fulfil the criteria for behavioural variant frontotemporal dementia (bv-FTD). The cognitive profiles of ALS and bv-FTD have been reported to be comparable, but this has never been systematically investigated. We aimed to determine the cognitive profile of bv-FTD and examine its similarities with that of ALS, to provide evidence for the existence of a cognitive disease continuum encompassing bv-FTD and ALS. We therefore systematically reviewed neuropsychological studies on bv-FTD patients and healthy volunteers. Neuropsychological tests were divided in 10 cognitive domains and effect sizes were calculated for all domains and compared with the cognitive profile of ALS by means of a visual comparison and a Pearson’s r correlation coefficient. We included 120 studies, totalling 2425 bv-FTD patients and 2798 healthy controls. All cognitive domains showed substantial effect sizes, indicating cognitive impairment in bv-FTD patients compared to healthy controls. The cognitive domains with the largest effect sizes were social cognition, verbal memory and fluency (1.77–1.53). The cognitive profiles of bv-FTD and ALS (10 cognitive domains, 1287 patients) showed similarities on visual comparison and a moderate correlation 0.58 (p=0.13). When social cognition, verbal memory, fluency, executive functions, language and visuoperception were considered, i.e. the cognitive profile of ALS, Pearson’s r was 0.73 (p=0.09), which raised to 0.92 (p=0.03), when language was excluded in this systematic analysis of patients with a non-language subtype of FTD. The cognitive profile of bv-FTD consists of deficits in social cognition, verbal memory, fluency and executive functions and shows similarities with the cognitive profile of ALS. These findings support a cognitive continuum encompassing ALS and bv-FTD.

Other media/resources:
(Lecture) Pathology and current molecular classification of ALS/FTD
https://www.youtube.com/watch?time_continue=23&v=n9WqUhY5Vqo
Presenter: Dr. Tibor Hortobagyi, Department of Neuropathology, University of Debrecen, Hungary
Dr. Hortobagyi provides a summary of neuropathological findings in ALS from the first candidate disease proteins and genetic mutations to the current state of knowledge, noting common biomarkers of ALS and FTD. This video begins with a general discussion of the importance of neuropathological investigation for the classification of neurodegenerative disorders.

(Seminar) Caregiver education program for ALS-FTD
https://www.youtube.com/watch?v=p-Rv3FDjEoQ
Presenter: Dr. Susan Walsh, ALS Association of Greater Philadelphia, USA
This is the first part of a three-part educational seminar for caregivers of individuals with ALS and FTD. In this segment, Dr. Walsh provides background information on ALS and FTD. The second and third segments (also available on Youtube) describe practical strategies with respect to behaviour management and problem solving.

(Handout) ALS & Cognitive Changes
https://www.als.ca/wp-content/uploads/2017/04/ALSCAN-Cognitive-Changes-EN.pdf
Publisher: ALS Society of Canada
This handout provides basic information on cognitive and behavioural changes in ALS. In the past, individuals with ALS and their caregivers have reported feeling under-informed about cognitive changes [28], so a handout such as this one can be helpful in starting important conversations about what to expect.

Further Reading:
Ferrari, R., Kapogiannis, D., D. Huey, E., & Momeni, P. (2011). FTD and ALS: A tale of two diseases. Current Alzheimer Research, 8(3), 273-294. http://doi.org/10.2174/156720511795563700
Giordana, M. T., Ferrero, P., Grifoni, S., Pellerino, A., Naldi, A., & Montuschi, A. (2011). Dementia and cognitive impairment in amyotrophic lateral sclerosis: A review. Neurological Sciences, 32(1), 9-16. http://doi.org/10.1007/s10072-010-0439-6
Goldstein, L. H., & Abrahams, S. (2013). Changes in cognition and behaviour in amyotrophic lateral sclerosis: Nature of impairment and implications for assessment. The Lancet Neurology, 12(4), 368-380. http://doi.org/10.1016/S1474-4422(13)70026-7
Lillo, P., & Hodges, J. R. (2010). Cognition and behaviour in motor neurone disease. Current Opinion in Neurology, 23(6), 638-642. http://doi.org/10.1097/WCO.0b013e3283400b41
Tsermentseli, S., Leigh, P. N., & Goldstein, L. H. (2012). The anatomy of cognitive impairment in amyotrophic lateral sclerosis: More than frontal lobe dysfunction. Cortex, 48(2), 166-182. http://doi.org/10.1016/j.cortex.2011.02.004

References:
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2.Miller, R. G., Munsat, T. L., Swash, M., & Brooks, B. R. (1999). Consensus guidelines for the design and implementation of clinical trials in ALS. Journal of the Neurological Sciences, 169(1–2), 2-12. http://doi.org/10.1016/S0022-510X(99)00209-9
3.Brooks, B. R., Miller, R. G., Swash, M., & Munsat, T. L. (2000). El Escorial revisited: Revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotrophic Lateral Sclerosis, 1(5), 293-299. http://doi.org/10.1080/146608200300079536
4.de Carvalho, M., Dengler, R., Eisen, A., England, J. D., Kaji, R., Kimura, J., … Swash, M. (2008). Electrodiagnostic criteria for diagnosis of ALS. Clinical Neurophysiology, 119(3), 497-503. http://doi.org/10.1016/j.clinph.2007.09.143
5.Ludolph, A., Drory, V., Hardiman, O., Nakano, I., Ravits, J., Robberecht, W., & Shefner, J. (2015). A revision of the El Escorial criteria – 2015. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 16(5–6), 291-292. http://doi.org/10.3109/21678421.2015.1049183
6.Al-Chalabi, A., Hardiman, O., Kiernan, M. C., Chiò, A., Rix-Brooks, B., & van den Berg, L. H. (2016). Amyotrophic lateral sclerosis: Moving towards a new classification system. The Lancet Neurology, 15(11), 1182-1194. http://doi.org/10.1016/S1474-4422(16)30199-5
7.Swinnen, B., & Robberecht, W. (2014). The phenotypic variability of amyotrophic lateral sclerosis. Nature Reviews Neurology, 10(11), 661-670. http://doi.org/10.1038/nrneurol.2014.184
8.Phukan, J., Elamin, M., Bede, P., Jordan, N., Gallagher, L., Byrne, S., … Hardiman, O. (2012). The syndrome of cognitive impairment in amyotrophic lateral sclerosis: A population-based study. Journal of Neurology, Neurosurgery and Psychiatry, 83(1), 102-108. http://doi.org/10.1136/jnnp-2011-300188
9.Ringholz, G. M., Appel, S. H., Bradshaw, M., Cooke, N. A., Mosnik, D. M., & Schulz, P. E. (2005). Prevalence and patterns of cognitive impairment in sporadic ALS. Neurology, 65(4), 586-590. http://doi.org/10.1212/01.wnl.0000172911.39167.b6
10.Barson, F. P., Kinsella, G. J., Ong, B., & Mathers, S. E. (2000). A neuropsychological investigation of dementia in motor neurone disease (MND). Journal of the Neurological Sciences, 180(1–2), 107-113. http://doi.org/10.1016/S0022-510X(00)00413-5
11.Beeldman, E., Raaphorst, J., Twennaar, M. K., De Visser, M., Schmand, B. A., & De Haan, R. J. (2016). The cognitive profile of ALS: A systematic review and meta-analysis update. Journal of Neurology, Neurosurgery and Psychiatry, 87(6), 611-619. http://doi.org/10.1136/jnnp-2015-310734
12.Phukan, J., Pender, N. P., & Hardiman, O. (2007). Cognitive impairment in amyotrophic lateral sclerosis. Lancet Neurology, 6, 994-1003. http://doi.org/10.1016/S1474-4422(07)70265-X
13.Gibbons, Z. C., Richardson, A., Neary, D., & Snowden, J. S. (2008). Behaviour in amyotrophic lateral sclerosis. Amyotrophic Lateral Sclerosis, 9(2), 67-74. http://doi.org/10.1080/17482960701642437
14.Girardi, A., MacPherson, S. E., & Abrahams, S. (2011). Deficits in emotional and social cognition in amyotrophic lateral sclerosis. Neuropsychology, 25(1), 53-65. http://doi.org/10.1037/a0020357
15.Strong, M. J., Grace, G. M., Freedman, M., Lomen-Hoerth, C., Woolley, S., Goldstein, L., … Figlewicz, D. (2009). Consensus criteria for the diagnosis of frontotemporal cognitive and behavioural syndromes in amyotrophic lateral sclerosis. Amyotrophic Lateral Sclerosis, 10(3), 131-146. http://doi.org/10.1080/17482960802654364
16.Canosa, A., Pagani, M., Cistaro, A., Montuschi, A., Iazzolino, B., Fania, P., … Chio, A. (2016). 18F-FDG-PET correlates of cognitive impairment in ALS. Neurology, 86, 44-49. http://doi.org/10.1212/WNL.0000000000002242
17.Lillo, P., Mioshi, E., Burrell, J. R., Kiernan, M. C., Hodges, J. R., & Hornberger, M. (2012). Grey and white matter changes across the amyotrophic lateral sclerosis-frontotemporal dementia continuum. PLoS ONE, 7(8). http://doi.org/10.1371/journal.pone.0043993
18.Strong, M. J. (2001). Progress in clinical neurosciences: The evidence for ALS as a multisystems disorder of limited phenotypic expression. Canadian Journal of Neurological Sciences, 28, 283-298. http://doi.org/10.1017/S0317167100001505
19.Agosta, F., Valsasina, P., Riva, N., Copetti, M., Messina, M. J., Prelle, A., … Filippi, M. (2012). The cortical signature of amyotrophic lateral sclerosis. PLoS ONE, 7(8). http://doi.org/10.1371/journal.pone.0042816
20.Agosta, F., Canu, E., Valsasina, P., Riva, N., Prelle, A., Comi, G., & Filippi, M. (2013). Divergent brain network connectivity in amyotrophic lateral sclerosis. Neurobiology of Aging, 34(2), 419-427. http://doi.org/10.1016/j.neurobiolaging.2012.04.015
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22.DeJesus-Hernandez, M., Mackenzie, I. R., Boeve, B. F., Boxer, A. L., Baker, M., Rutherford, N. J., … Rademakers, R. (2011). Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron, 72(2), 245-256. http://doi.org/10.1016/j.neuron.2011.09.011
23.Renton, A. E., Majounie, E., Waite, A., Simón-Sánchez, J., Rollinson, S., Gibbs, J. R., … Traynor, B. J. (2011). A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD. Neuron, 72(2), 257-268. http://doi.org/10.1016/j.neuron.2011.09.010
24.Olney, R. K., Murphy, J., Forshew, D., Garwood, E., Miller, B. L., Langmore, S., … Lomen-Hoerth, C. (2005). The effects of executive and behavioral dysfunction on the course of ALS. Neurology, 65(11), 1774-1777. http://doi.org/10.1212/01.wnl.0000188759.87240.8b
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The Role of Biomarkers in Diagnosing Alzheimer's Disease

Contributor: Yosefa Allegra Ehrlich, M.Phil., Ph.D. Candidate in Clinical Psychology, Queens College, City University of New York (CUNY)

Posted: 4/18/18

Overview: The use of biomarkers in diagnosing Alzheimer’s disease (AD) in vivo is gaining popularity. Biomarkers refer to measureable characteristics that indicate the presence of biologic and/or pathologic processes (1). This shift is based on an expanding body of research indicating that in vivo methods validly estimate post-mortem AD pathologic changes that characterize the disease (2–5). The goals of incorporating biomarkers into the diagnosis include: clarifying the multifaceted etiology/pathophysiology (5), standardizing research terms (4,6), identifying individuals in preclinical phases (6,7), improving diagnostic accuracy (8), and developing more precise interventions (9).

Over the past decade, the National Institute on Aging and Alzheimer’s Association (NIAA-AA) and the International Working Group (IWG) have continually proposed new criteria for the diagnosis of AD. There are points of overlap as well as divergence between their suggested frameworks. In a position paper still under review (5), the NIAA-AA propose a definition of AD based on evidence of three biological markers of pathologic processes: (a) b-amyloid plaques, (b) phosphorylated tau (P-tau), (c) markers of neuronal injury (e.g., elevated CSF total tau (T-tau)) and cerebral hypometabolism and atrophy (10). Presence of b-amyloid deposition alone (with normal tau and no neurodegeneration) is considered Alzheimer’s pathologic change. A diagnosis of AD indicates evidence of both b-amyloid and P-tau. Neuronal injury is understood to emerge as a consequence of b-amyloid and tau and is not specific to AD pathology (11); accordingly, those markers are conceptualized to emerge in the later stages of the disease continuum and are not essential for a diagnosis. Importantly, these diagnostic states exist independently of clinical symptoms. The authors recognize that cognitive impairment generally corresponds with increased presence of biomarkers and suggest that clinical changes should be measured along six stages of increasing impairment with the first stage beginning with positive evidence of biomarkers. This framework reflects an effort to disentangle the presence of AD neuropathology (disease process) from the clinical syndrome (signs/symptoms).

The IWG-2 criteria for AD (9,3,2,12,13) also require in vivo evidence of pathology ((a)decreased CSF b-amyloid and increased CSF P- and T-tau, (b) increased amyloid PET, (c) AD autosomal mutation) for an AD diagnosis. Similar to the NIA-AA, the IWG-2 agrees that biomarkers can be detectable in pre-clinical (asymptomatic) states and calls for a continuum-based understanding of disease course. However, the groups’ definitions diverge regarding clinical phenotypic expression. The IWG-2 criteria call for evidence of cognitive disturbance, primarily episodic memory impairment, in issuing a diagnosis. In the preclinical stage, patients with MCI and positive biomarkers receive a diagnosis of MCI due to AD or, interchangeably, prodromal AD (3,12,13). A diagnosis of typical AD is only distinguished by the degree of cognitive impairment (2). By including the cognitive criterion in the diagnosis, the authors are conceptualizing AD as both biological and syndromic (clinico-pathological).

While the definition of an AD diagnosis remains unresolved, the utility and validity of biomarkers measured via imaging and CSF markers have been well demonstrated (14). Fibrillary b-amyloid deposition associated with AD can be validly and reliably measured in vivo via increased amyloid PET binding and low CSF Ab42 (15). Pathologic tau deposition in AD can be assessed through newly developed PET ligands that show elevated cortical tau binding as well as elevated P-tau CSF markers (15,16). Neuronal injury is measured via cortical atrophy (on MRI) and/or hypometabolism (on FDG PET) generally in medial temporal, medial parietal, and lateral temporal-parietal cortices (17).

Limitations of incorporating biomarkers in AD diagnoses apply in both research and clinical settings. Despite increased validity, none of the in vivo biomarker tools are as sensitive as histology (18). The lack of consensus among researchers on diagnostic criteria limits standardization and generalizability of findings. There are also no clear numeric cut-offs by which to categorize biomarker levels; while some have proposed continuous measurements (5), this complicates research standardization as well as clinical translation. The primary clinical concern surrounds poor specificity and sensitivity of biomarkers to clinical symptoms associated with AD (19). Between 30 to 40% of individuals with no cognitive impairment (asymptomatic) show biological abnormalities in vivo and on autopsy (20,21), while 10 to 30% of individuals with clinical signs of AD-related dementia have clean autopsies (22). This has led to the proposal of including additional factors (e.g., vascular) as biomarkers (23) to increase prognostic reliability. Further clinical challenges include prohibitive costs, limited accessibility, inconsistent regulatory approval, and uncertain insurance reimbursement (12). Finally, as in many areas of research, more population-based studies are needed to validate the utility of biomarkers in diverse ethnic groups (24).

Highlighted Abstract: Fluid and imaging biomarkers for Alzheimer’s disease: Where we stand and where to head to.

There is increasing evidence that a number of potentially informative biomarkers for Alzheimer disease (AD) can improve the accuracy of diagnosing this form of dementia, especially when used as a panel of diagnostic assays and interpreted in the context of neuroimaging and clinical data. Moreover, by combining the power of CSF biomarkers with neuroimaging techniques to visualize Aβ deposits (or neurodegenerative lesions), it might be possible to better identify individuals at greatest risk for developing MCI and converting to AD. The objective of this article was to review recent progress in selected imaging and chemical biomarkers for prediction, early diagnosis and progression of AD. We present our view point of a scenario that places CSF and imaging markers on the verge of general utility based on accuracy levels that already match (or even surpass) current clinical precision.

Henriques, A. D., Benedet, A. L., Camargos, E. F., Rosa-Neto, P., & Nóbrega, O. T. (2018). Fluid and imaging biomarkers for Alzheimer’s disease: Where we stand and where to head to. Experimental Gerontology, (January), 1-9. http://doi.org/10.1016/j.exger.2018.01.002

Other Media and Resources:
Webinar- Hear Clifford Jack, MD present his conceptualization of biomarker stages
https://www.alzforum.org/webinars/together-last-top-five-biomarkers-model-stages-ad

Webinar- Learn about the development and uses of the AlzBiomarker database
https://www.alzforum.org/webinars/learn-about-ad-biomarker-meta-analysis-alzbiomarker-database

Webinar- Biomarkers, cognition, and cognitive reserve in AD
https://www.labroots.com/webinar/biomarkers-cognition-and-cognitive-reserve-in-alzheimers-disease

Further Reading:
Bondi, M.W., Edmonds, E.C., Salmon, D.P., 2017. Alzheimer’s Disease: Past, Present, and Future. J. Int. Neuropsychol. Soc. 23, 818–831.

Frisoni, G. B., Boccardi, M., Barkhof, F., Blennow, K., Cappa, S., Chiotis, K., … Winblad, B. (n.d.). A Strategic Research Agenda to the Biomarker-Based Diagnosis of Prodromal Alzheimer’s Disease, 1–39. http://discovery.ucl.ac.uk/1567593/1/Frisoni_Strategic_roadmap_early_diagnosis.pdf

Frisoni, G. B., Boccardi, M., Barkhof, F., Blennow, K., Cappa, S., Chiotis, K., … Winblad, B. (2017). Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers. The Lancet Neurology, 16(8), 661–676. http://doi.org/10.1016/S1474-4422(17)30159-X

Jack, C. R. J., Bennet, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Elliot, C., … Sperling, R. (n.d.). NIA-AA Research Framework: Towards a Biological Definition of Alzheimer’s Disease. https://alz.org/aaic/_downloads/draft-nia-aa-7-18-17.pdf

Vanderschaeghe, G., Dierickx, K., Vandenberghe, R., 2018. Review of the Ethical Issues of a Biomarker-Based Diagnoses in the Early Stage of Alzheimer’s Disease. J. Bioeth. Inq. 1–12.

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