Cognitive impairments in inherited metabolic diseases: Promises and challenges

ABSTRACT

This is an introduction to the special issue on cognitive impairments in inherited metabolic diseases (IMD). It provides an overview of the studies included, focusing on the possibility of selective impairments which could provide unique evidence on the specificity of neural circuitries mediating cognitive functions. It will suggest that these circuitries have different metabolic properties which make them especially apt to carry out certain functions, but also particularly susceptible to certain forms of metabolic disruption. Knowledge of selective impairments is also crucial to properly evaluate the difficulties engendered by individual diseases and track treatment outcomes. IMR research holds the promise of a more complete understanding of cognition, from cellular functioning to behaviour and of further improvements in treatment. Advances, however, will require detailed assessments, comparisons across diseases, and the integration of different levels of explanation. This will be possible only through close collaborations between centres and types of professionals.

KEYWORDS:

• Metabolic impairments
• cognitive impairments
• specific impairments

The purpose of this special issue is to showcase a number of studies assessing cognitive functions in children and adults with inherited metabolic diseases (IMD) to promote a new, rapidly growing field of study that holds the promise of new discoveries about cognition and brain functions and advances in clinical management. In IMD, failures to synthesize different enzymes may disrupt cell functioning in a variety of ways. There may be an accumulation of substances that either are toxic per se or are waste products that become toxic when they accumulate, and/or there may be a reduced ability to synthesize essential compounds. Examples of disorders involving toxic accumulation of substances are lysosomal storage disorders affecting lipid metabolism (e.g., Niemen-Pick Type C, Fabry disease, Tay-Sachs, Gaucher’s disease) and carbohydrate metabolism (e.g., galactosaemia, Pompe disease; mucopolysaccharidosis II and IV). Examples of disorders that also reduce the synthesis of important compounds are disorders that disrupt protein metabolism (failure to metabolize certain amino acids) such as phenylketonuria (PKU) and tyrosinaemia (I–III). In PKU, there is a toxic accumulation of phenylalanine (Phe), but also a reduced synthesis of dopamine (as the result of reduced synthesis of the precursor tyrosine; see Landvogt et al., 2008); in tyrosinaemia there is a toxic accumulation of tyrosine, but also a reduced synthesis of serotonin (because of the competition with tyrosine and other amino acids; e.g., Thimm et al., 2011). Although some metabolic disorders affect a variety of different organs, neurological impairments are often one of the most significant consequences, possibly because neurons do not renew, and this makes them more fragile. Investigation of cognitive impairments in many of these disorders is only in its infancy because it is only recently that advances in our understanding of the biological causes of these impairments, coupled with advances in dietary management and in enzyme replacement therapy, have significantly prolonged the life and mental health of affected individuals. Moreover, it is only recently that detailed neuropsychological analyses have revealed impairments in groups of patients previously believed to be unaffected (e.g., see impairments in Gaucher type 1; Biegstraaten et al., 2012; mucopolysaccharidosis II, Blundell et al., 2018, and mild mucopolysaccharidosis IV, Crowe, Yaplito-Lee, Anderson, & Peters, 2017). However, studies that assess cognitive impairments in IMB are now starting to accumulate. It is time, therefore, that we take stock and begin to compare results across studies and types of impairments.

A lot of progress has been made in our understanding of the brain and of the cognitive functions that it supports through a consideration of brain lesions that affect particular structures, cortical areas, and their connections (see Rapp, 2001; Shallice, 2009). These lesions can be acquired in a variety of ways including concussion, degeneration, and stroke, but the common denominator is that they impinge on particular brain areas. Thus, since the end of the nineteenth century, traditional neuropsychology has used these lesions as a valuable tool to explore the relationship between cognition and the brain. More recently, since the second half of the twentieth century, the focus has been broadened to include developmental disorders like developmental dyslexia where there are no acquired brain lesions, but also here we may assume some structural/functional damage in areas that are normally involved in processing certain information (e.g., orthographic information for developmental dyslexia).

Impairments that arise from inherited metabolic disorders (IMD) are different because, at first blush, there is no reason to believe that these impairments would affect some populations of neurons more than others. If neuronal metabolism is disrupted, one might plausibly assume that all brain functions would be similarly affected. If, instead, impairments are selective, this would suggest that the metabolism of different populations of neurons is adapted to support specific cognitive functions. Either way, this information is fundamental to fill in the gaps between cellular functioning, brain areas, and cognitive functions. Finding specific impairments in metabolic diseases will also help us to further delineate the architecture of cognition by identifying functions that rely on dedicated neuronal circuitry. In IMD, the specificity of the cognitive impairments will be a direct reflection of the functional specificity of the affected neural circuitry. This is in contrast with what happens in stroke where damaged brain areas reflect the territory of affected vessels. Finally, an identification of the cognitive profiles that characterize different metabolic impairments is crucial to allow a sensitive assessment of the efficacy of current and future therapeutic interventions. The purpose of this special issue is to provide examples of types of evidence that can be provided by studies that assess cognitive functions in IMD and the type of inferences that we can start making.

Advancing our understanding of cognition and neuron metabolism

As mentioned above, one may start with the default hypothesis that a disruption of cellular metabolism will uniformly impair neuronal processing. Following this hypothesis, in any IMD, the severity of impairment in different tasks should exclusively be a consequence of task complexity and individual variation in aptitudes for different tasks. Knowledge of type of metabolic disease should make no contribution; hierarchies of impairments should be the same across metabolic diseases. This finding would still be important. It would help us to map task complexity and brain resources by identifying which tasks/cognitive functions are more dependent on overall brain prowess and/or rely on more extensive coordination of different brain areas. It would also tell us that neurons in different parts of the brain function largely in the same way and that the specialization of different brain areas is due to their anatomical localization and ease of connection with other areas rather than to differences at the cellular level. Alternatively, we may find out that severity of impairment across tasks and cognitive domains differs in different metabolic diseases. From the seminal work of Brodmann (Garey 2006), we know that there are cytoarchitectonic differences between different brain areas with neurons having different appearances and different distributions in the cortical layers of different brain areas. We also know that during gestation neurons differentiate and then migrate to different brain areas. However, we do not know what properties make them especially apt to carry out specific cognitive functions. For example, do neurons in the frontal lobes have special characteristics that make them particularly apt to integrate information across inputs or is it simply that they have extensive connections with other brain areas? Similarly, do the neurons in the parietal lobe have special characteristics that make them particularly apt to find a stimulus in a visual display or does this simply depend on connections with occipital visual areas? Finding different profiles of cognitive impairments in different metabolic diseases will provide a first indication that metabolism differs in cells that specialize for different cognitive functions. Studies from the literature as well as some of the articles in this special issue provide preliminary indications in this direction. Both studies comparing cognitive profiles across diseases and studies showing specific impairments in certain diseases are directly relevant.

Blundell et al. (2018) have compared cognitive profiles in children with tyrosinaemia III and mucopolysaccharidosis (MPS IV or Morquio syndrome). Children with both diseases are slower than controls in the simple detection of visual targets and visual search tasks, but in the context of different cognitive profiles. When asked to track a visual target, children with Morquio have a problem in maintaining fixation and show a higher number of intrusive saccades than control children, while children with tyrosinaemia show no or mild difficulties. In language tasks (word comprehension and picture naming) the pattern is reversed: Children with tyrosinaemia show significant impairments while children with Morquio show no or mild difficulties. These results are consistent with previous results from the literature showing that children with tyrosinaemia have specific difficulties with language tasks (Bendadi et al., 2014; Thimm et al., 2012). The tyrosinaemia profile contrasts not only with what is seen in Morquio, but also with what is seen in PKU, where language difficulties are mild or absent, but where, instead, there are more severe difficulties in allocating visual attention (see De Felice, Romani, Geberhiwot, MacDonald, & Palermo, 2018; Romani, MacDonald, et al., 2017).

Again, however, the visual impairments in PKU and Morquio appear different. In Romani, MacDonald, et al. (2018) we show that, in a sample of adult patients with PKU (AwPKU), differences from controls increase progressively with number of distractors. This is what is expected given a slower/less efficient allocation of serial attention where delays sum across sampled positions. This is not found by Blundell et al., in children with Morquio. That is, reaction times (RTs) increase with number of distractors, but differences with controls remain constant; there is no difference in the efficiency slope. What explains this pattern is not completely clear. One possibility suggested by the authors is a delay in making decisions. This could be a common occurrence seen across diseases and in ageing (we suggest the contribution of a similar difficulty in PKU). Alternative possibilities could be a more peripheral difficulty in maintaining fixation, which would produce an occasional delay in stimulus identification or a difficulty with sustained attention (but this would produce occasionally very long RTs, which is not found). More research is needed to explore these impairments, but it is clear that current evidence points to differences across diseases.

Other results indicate selective impairments within metabolic disease types. Consistent with a specific difficulty in allocating visual attention, AwPKU do not show the same disproportionate increase of RTs with difficulty of condition in picture naming that they show in visual search (e.g., comparing differences in frequency/regularity in picture naming vs. number of distractors in visual search; Romani, MacDonald, et al., 2018). In addition, not only language but also memory and learning (including spelling, which requires memorization of irregular words in English) appear relatively preserved. These functions may be sub-optimal in individuals with poor dietary control, but they do not show the significant group impairment shown in tasks requiring visual attention and executive functions (EFs; see Palermo et al., 2017; Romani et al., 2017). A similar pattern—with more severe impairments in visual attention than in language tasks (particularly lexical-retrieval tasks)—is seen in ageing, which is also characterized by a degradation of the myelin sheet, important for the speed of axonal transmission (e.g., see Jenkins, Myerson, Joerding, & Hale, 2000). These results argue against a homogeneous reduction in the efficiency of axonal transmission across functions. Crowe et al. (2017) also show that visuoattentional skills may be affected in MPS II (Hunter syndrome), in the context of generally preserved cognition. These results suggest that neurons in posterior/parietal areas of the brain responsible for visual attention may be more susceptible to processes of degradation common to these diseases.

Selective patterns of impairments are also seen in other diseases not covered in this issue. There is a suggestion of specific language impairments in galactosaemia with deficits affecting phonological awareness (e.g., Lewis, Coman, Syrmis, Kilcoyne, & Murdoch, 2013), lexical access (Antshel, Epstein, & Waisbren, 2004; Potter, Lazarus, Johnson, Steiner, & Shriberg, 2008; Timmers et al., 2012), and phonological retrieval/articulatory production (e.g., Robertson, Singh, Guerrero, Hundley, & Elsas, 2000; Waisbren et al., 2012; Webb, Singh, Kennedy, & Elsas, 2003). A recent study reported that, in a small cohort of patients with galactosaemia, an index of white matter pathology (neurite orientation dispersion index or ODI) showed mainly an increase in the left hemisphere with abnormalities in the uncinate and arcuate fasciculi, structures strongly involved in language processing (Timmers et al., 2015). This cognitive profile contrasts with a lack of language impairments in PKU, but also in other diseases. For example, there is a suggestion that Gaucher type II and III disease affects particularly visuospatial functions (Goker-Alpan et al., 2008), and this may relate to damage to hippocampal regions reported in this disease (Wong et al., 2004). Finally, impairments in speed of processing are seen across several diseases—for example, in PKU (Albrecht, Garbade, & Burgard, 2009; Romani, MacDonald, et al., 2018), Gaucher disease (Biegstraaten et al., 2012), and galactosaemia (Antshel et al., 2004; Timmers, 2012)—and they are wide-spread in ageing. These impairments may be related to white matter pathology, which are present across these diseases and in ageing (for PKU: Anderson & Leuzzi, 2010; for galactosemia: Timmers et al., 2015; for Gaucher disease Davies, Seunarine, Banks, Clark, & Vellodi, 2011; for ageing: Kerchner et al., 2012). However, the specific patterns of impairment in these conditions suggest that white matter deficits may be of different kinds, impinge on different areas, and/or be coupled with other associated deficits, which may explain limitations in the associations between white matter lesions and cognitive impairments (e.g., Anderson et al., 2007). Selective deficits of executive functions, despite their being involved in a wide range of tasks, are discussed below.

Taken together, these results indicate that different metabolic diseases impact on cognition in diverse ways. This opens the possibility of exciting new research avenues. Future research should: (a) consolidate knowledge of different cognitive impairments in IMD; (b) understand what is in common between diseases that produce similar cognitive impairments; (c) relate these impairments to damage of specific neuronal circuitry; (d) understand how the metabolic properties of different neuronal circuitries relate to their cognitive functions.

Advancing our understanding of the architecture of cognition: The case of EFs

Metabolic impairments may also help with our understanding of cognitive functions themselves. One example may be executive functions (EFs), which are commonly impaired across diseases. EFs are concerned with overseeing behaviour according to set goals/priorities and include sub-functions such as (a) planning/maintaining a goal, (b) switching (the ability to be flexible and change response according to task demands), (c) monitoring (the ability to track performance and update information in working memory), and (d) sustained attention (the ability to keep being on task). The problem with EFs is their intrinsic complexity. They need to operate across modalities and depend on the coordination and integration of inputs and outputs from more peripheral brain areas. This complexity, rather than a specific susceptibility to damage, may make impairments of EFs common across diseases. This leaves open the question whether EFs are specific functions that rely on specific neural resources (e.g., with a hub in the frontal lobes) or whether, alternatively, they are only descriptive labels that identify complex tasks where coordination of functions across different brain areas is needed, but where there is no deployment of centralized resources. Analyses of impairments in metabolic diseases may provide important evidence. The hypothesis that impairments of EF only reflect the amount of general brain resources available for processing predicts that EF should always be the most affected functions. The more severe the disease, the worse the impairment in EFs. Other functions, however, should also be affected to a degree, if the disease is severe enough and/or the tasks used are sensitive enough. On the other hand, finding that EFs are particularly impaired in some diseases and/or that impairments cannot be interpreted as an accumulation of minor impairments across other functions will indicate that EFs are functions in their own right, mediated by specific brain resources.

Studies have shown that EFs are commonly affected across metabolic diseases. In this issue, Crowe et al. have reported executive impairments in children with mucopolysaccharidosis type II (MPS II or Hunter syndrome; with impairments in the Tower of London and high rates of impulsive errors). Bisiacchi, Tarantino, Mento, & Burlina, 2018 have reported impairments in children with PKU (in Trial Making Test B and Backwards Digit Span), and De Felice et al. have reported impairments in adults with PKU across a number of tasks commonly used to assess EFs (with impairments in planning: Tower of Hanoi and semantic fluency; switching: the Wisconsin Card Sorting Task; monitoring, sustained attention, and working memory; see also Palermo et al., 2017). Finally, Bergeron et al. have reported impairments in Niemann–Pick C, again, in the Trail Making B and in span tasks involving monitoring. However, there is also evidence that impairments of EFs may be quite selective, affecting EF and not other functions.

The adults with PKU (AwPKU) studied by De Felice et al. also showed impairments in complex language tasks requiring EFs (such as deficits in planning a narrative, comprehending metaphors, making inferences, and completing a sentence with an unrelated word, as in the Hayling Sentence Completion Task). Crucially, however, these impairments could not be interpreted as the accumulation of smaller impairments in more basic language functions (normal performance in lexical access, phonological input and output processing, semantic and syntactic processing). Performance on these basic language functions was unimpaired even when assessed with sensitive measures (e.g., RTs in picture naming) or in the context of a complex task (e.g., error rates, word production rate, and mean utterance length in a narrative task). These results suggest selective impairments of EFs.

Bisiacchi et al. (2018) showed that children with PKU showed more selective impairments of EFs than a group of children who were congenitally HIV positive. Compared to controls, the PKU children were impaired in executive tasks such as the Backward Digit Span and the Trail Making Test B. Moreover, in these tasks, they performed worse than the HIV-positive children, while performance in other tasks did not differ (Raven, Street’s completion test, picture naming, The Tower of London, and phonemic fluency).

Bergeron, Poulin, and Laforce (2018) showed that EFs may be the first functions to be impaired in individuals with adult Niemann–Pick disease type C, but that progression and relation to structural damage is different in this disease and in Alzheimer disease (AD) in spite of a number of other commonalities. Both diseases involve hypometabolism of frontal areas, neurofibrillary tangles (NFT) due to the accumulation of tau proteins, abnormal amyloid-β metabolism (although there are no abnormal amyloid plaques in NPC), and impaired intracellular cholesterol trafficking. In both diseases, impairments of EFs and memory impairments are eventually present, but progression is different. AD shows early cortical damage involving the parieto-temporal “default-mode network”, coupled by early memory impairments. NPC, instead, shows early damage to subcortical structures involving the cerebellum, the thalamus, the hippocampus, and the striatum, but also early hypo-metabolism of frontal areas, coupled with early executive deficits. Memory impairments arise only at a later stage (see also Heitz, Epelbaum, & Nadjar, 2017¹). These results suggest that frontal processing mediating EF may be disrupted by reduced input from subcortical structures as much as by direct damage. Furthermore, they suggest that the frequency of executive impairments across diseases is not simply the result of frontal regions having higher metabolism due to their being a highly active, central hub of brain connectivity, but, instead, that frontal areas may have characteristics that may make them especially sensitive to certain types of metabolic damage (e.g., in the case of NPC, because of reduced input from dopaminergic striatal–cortical connections).

Taken together, results from IMD suggest two conclusions: (a) EFs rely on extensive brain resources, so that even a minor depletion has negative consequences (explaining the frequency of EF deficits across diseases); but also (b) EFs are specific functions that rely on dedicated brain resources with particular biochemical properties so that they can be selectively affected in certain metabolic diseases.

Improving clinical management and understanding of individual diseases

Finally, detailed assessments of cognitive functions are important to better understand and manage individual diseases. The efficacy of current and new treatments cannot be properly evaluated in the absence of detailed cognitive assessments. This is especially true if not all cognitive functions are equally affected across diseases and developmental stages. The study by Crowe et al. (2017) is a good example of the use of a detailed neuropsychological assessment to investigate the efficacy of enzyme replacement therapy in children with mucopolysaccharidosis type II (MPS II or Hunter syndrome). The authors find only mild, inconsistent cognitive impairments in these children, and no differences from baseline after two years of treatment. We do not know, however, whether performance might have worsened with no treatment.

We need to improve our neuropsychological tools. We need batteries with tasks that:

1. are sensitive to impairments in individual diseases;

2. can be performed at different ages;

3. can be repeated (because either they are not very sensitive to practice effects or they can be administered in parallel versions) and have norms to assess the effects of repeated performance (see Heilbronner et al., 2010; McCaffrey & Westervelt, 1995; Salinsky, Storzbach, Dodrill, & Binder, 2001);

4. have norms both for control children across ages and for children with the same disease.

The paper by Blundell et al. (2018) provides an example of how developmental, task-specific charts could be used to track both the performance of a group with a disease and an individual’s performance if tests are repeated. The conjoined availability of sensitive, repeatable task batteries and developmental charts will allow us to better evaluate whether differences in treatment (e.g., a new diet, a new enzymatic treatment) change cognitive trajectories. It will also allow us to better evaluate whether the impact of toxicity changes at different ages and to assess whether there are critical periods where treatment (including dietary compliance) may be especially important for the proper development of certain cognitive functions.

Throughout this introduction I have stressed the importance of comparing impairments across diseases to increase our understanding of cognition and its realization in the brain. These comparisons are also important to understand individual diseases. For example, Bergeron et al. have stressed how an understanding of Niemann–Pick C can improve our understanding of Alzheimer’s disease (e.g., stressing the role of dysfunctional cholesterol trafficking in causing cognitive impairments in both diseases). Blundell et al. have shown how the similarity of cognitive impairments in tyrosinaemia I and III allows us to conclude that the deficits seen in tyrosinaemia I are not due to the treatment used in this disease (2-nitro-4-trifluromethylbenzoyl or NTBC), which blocks the action of certain enzymes turning the biochemical profile of tyrosinaemia I into that of tyrosinaemia III, but more likely to the high levels of tyrosine that are common to both diseases.

Conclusions

I hope that this introduction and especially the papers included in this special issue provide some indication of what can be achieved through a more detailed assessment of cognitive impairments in IMD and that they will inspire a new generation of studies. This research is challenging given that disorders are rare or very rare and that the clinical teams caring for these patients have limited resources. However, this research can be very rewarding, improving our understanding of cognition, as well as the management of specific diseases. Collaborations across centres, across different professionals, and across levels of explanation (from cellular biology to cognition) will hold the key to success.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1 Note that these authors report early deficits in “attention”. However, this refers to sustained attention and monitoring, not to visual attention (the tasks involved are verbal and pictorial span tasks). It is important to not confound the two.