Metabolic pathways in the periphery and brain: Contribution to mental disorders?

Accepted Manuscript Title: Metabolic pathways in the periphery and brain: contribution to mental disorders? Author: Andrzej Nagalski Kamil Kozinski Marta B. Wisniewska PII: DOI: Reference:

S1357-2725(16)30274-6 http://dx.doi.org/doi:10.1016/j.biocel.2016.09.012 BC 4984

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The International Journal of Biochemistry & Cell Biology

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16-5-2016 14-9-2016 15-9-2016

Please cite this article as: Nagalski, Andrzej., Kozinski, Kamil., & Wisniewska, Marta B., Metabolic pathways in the periphery and brain: contribution to mental disorders?.International Journal of Biochemistry and Cell Biology http://dx.doi.org/10.1016/j.biocel.2016.09.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Metabolic pathways in the periphery and brain: contribution to mental disorders? Andrzej Nagalski,1, * Kamil Kozinski,1, * Marta B Wisniewska1, *, † 1

Laboratory of Molecular Neurobiology, Centre of New Technologies, University of Warsaw, 02097 Warsaw, Poland * Equal contribution † Corresponding author: [email protected] Graphical abstract

List of 5 keywords: mental disorders, diabetes, genetic risk factors, insulin, inflammation Abbreviations list: ACCORD-MIND - Action to Control Cardiovascular Risk in Diabetes—Memory in Diabetes; BBB blood-brain barrier; BD - bipolar disorder; BDNF - brain-derived neurotrophic factor; DISC1 disrupted in schizophrenia 1; GLUT - glucose transporter; GSK3 - glycogen synthase kinase 3; GWAS - genome-wide association study; HPA - hypothalamic-pituitary-adrenal; IL - interleukin; INSR - insulin receptor; MAPK - mitogen-activated protein kinase; MD - major depression; MRI magnetic resonance imaging; NHGRI-EBI - National Human Genome Research Institute-European 1

Bioinformatics Institute; NPY - neuropeptide Y; SCZ - schizophrenia; SMI - serious mental illness; T2D - type 2 diabetes; SNP - single-nucleotide polymorphism; TCF7L2 - transcription factor 7-like 2; TNFα - tumor necrosis factor α

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Abstract The association between mental disorders and diabetes has a long history. Recent large-scale, well-controlled epidemiological studies confirmed a link between diabetes and psychiatric illnesses. The scope of this review is to summarize our current understanding of this relationship from a molecular perspective. We first discuss the potential contribution of diabetes-associated metabolic impairments to the etiology of mental conditions. Then, we focus on possible shared molecular risk factors and mechanisms. Simple comorbidity, shared susceptibility loci, and common pathophysiological processes in diabetes and mental illnesses have changed our traditional way of thinking about mental illness. We conclude that schizophrenia and affective disorders are not limited to an imbalance in dopaminergic and serotoninergic neurotransmission in the brain. They are also systemic disorders that can be considered, to some extent, as metabolic disorders.

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1. INTRODUCTION The high prevalence between schizophrenia (SCZ) and diabetes was first observed by Sir Henry Maudsley in 1879. In the 1920s and 1930s, a series of preliminary studies of small groups of patients revealed higher glucose levels, diabetes-like glucose tolerance curves, and frequent insulin resistance in patients with “dementia precox,” which SCZ was referred to at the time (Kohen, 2004). Also at that time, insulin-shock therapy was introduced by Manfred Sakel to treat dementia precox by inducing coma and convulsions (Sakel, 1994). Soon afterward, clinicians noticed that some patients required more insulin to achieve seizures (Meduna et al., 1942), further corroborating the link between diabetes and SCZ. In recent decades, many potential biological risk factors and mechanisms of diabetes and serious mental illnesses (SMIs) have been proposed. This was accompanied by an expansion of the identification of genetic risk factors based on genome-wide association studies (GWASs) and other large cohort analyses. Combining genetic and biological risk factors between diabetes and SMIs led to tentative evidence that both diseases might share a common pathophysiological nexus. This review examines and discusses the potential factors that are responsible for the observed comorbidity, including the effects of impaired glucose metabolism on brain function, genetic risk factors, and potential pathophysiological mechanisms.

1.1. Serious mental illnesses: schizophrenia, bipolar disorder, and major depression Schizophrenia, bipolar disorder (BD), and major depression (MD) are three psychiatric disorders that are often described under the term SMIs. These disorders are separate disease entities with multifactorial and poorly understood etiology and heterogeneous symptoms (Insel, 2010; Kato, 2008; Kavanagh et al., 2015; Wong and Licinio, 2001). They are diagnosed according to established criteria, but the current biological and medical view is that they lie on a continuum with overlapping phenotypes. Major depression is characterized by low mood, anhedonia, fatigue, psychomotor retardation, ideas of guilt and unworthiness, and, in cases of psychotic depression, hallucinations. All of these symptoms occur in BD and SCZ. In BD, the periods of depression alternate with periods of mania that manifest as heightened energy and mood. Schizophrenia is the most severe disorder of these three. Patients with SCZ suffer from negative and positive symptoms and cognitive impairments. Negative symptoms include anhedonia, 4

flattened affect, and asociality. Positive symptoms include delusions, hallucinations, disorganized thinking and speech, agitation, and catatonia. Lying between these illnesses is schizoaffective disorder, which is a combination of positive symptoms and mood alterations. Serious mental illnesses are generally accepted to be caused by impairments in monoaminergic systems in the brain, and multiple genetic, biological, and social risk factors contribute to the development of these conditions (Green et al., 2015; Kato, 2008; Kavanagh et al., 2015). However, the mechanisms that link these risk factors with etiology and the actual manifestations of SMIs need to be discovered.

1.2. Type 2 diabetes Diabetes is the most common metabolic disorder, occurring in one out of every 11 adults. It is characterized by chronically elevated glucose levels in the blood as a result of deficits in insulin secretion or insulin action (Tripathy and Chavez, 2010). In healthy people, high blood glucose levels after a meal induce the secretion of insulin by β-cells in the pancreas (Del Prato et al., 2002). Glucose is then absorbed from the blood by insulin-sensitive cells (primarily skeletal muscle cells, adipocytes, and liver cells) to be stored as glycogen or fat. This mechanism is disturbed in people with diabetes, ultimately leading to hyperglycemia, which is harmful to many organs and tissues (Tuomi et al., 2014). There are two main types of this disease. Type 2 diabetes (T2D) is the most common form of diabetes, which results from a reduction of the sensitivity of target organs to the actions of insulin (Tuomi et al., 2014). Such insulin resistance is followed by a failure of pancreatic β-cells to produce and secrete sufficient insulin. Similar to SMIs, T2D is a complex polygenic disorder that is triggered by both genetic and environmental risk factors, the pathophysiology of which is still not fully understood.

1.3. Comorbidity of diabetes and serious mental illnesses Looking at the association between diabetes and SMIs, meta-analyses have shown that people who suffer from any of these three SMIs develop T2D at least two-times more often than the general population (Anderson et al., 2001; Annamalai and Tek, 2015; Asghar et al., 2007; Knol et al., 2006; Mezuk et al., 2008; Roy and Lloyd, 2012; Vancampfort et al., 2013). The prevalence of T2D in individuals with SMIs may be even higher. An estimated 70% of cases of T2D in people 5

with SMIs are undiagnosed, contrasting with 25-30% in the general population (Taylor et al., 2005; Voruganti et al., 2007). Although the prevalence of T2D in SMIs is generally accepted, antipsychotics, moodstabilizing medications, and an unhealthy lifestyle are often blamed for this comorbidity, rather than a direct link between these disorders. Numerous studies have addressed the problem of obesity, metabolic syndrome, and diabetes as side effects of psychoactive medications (Holt and Peveler, 2009; Rojo et al., 2015). Differentiating between the effects of antipsychotic medications and the medication-independent association between T2D and SMIs is difficult because virtually all psychiatric patients receive pharmaceutical treatment. Two pieces of evidence, however, corroborate the latter possibility (although they do not necessarily exclude the former). First, the bidirectional relationship between depression and T2D has been consistently reported (Chen et al., 2013; Pan et al., 2012; Rotella and Mannucci, 2013) and was recently supported by a significant genetic correlation (Kan et al., 2016). In SCZ (or BD), it is not possible to observe a bidirectional relationship because these diseases affect mostly young people who are unlikely to have established T2D. Nevertheless, several studies reported lower glucose tolerance in firstepisode and drug-naive SCZ patients compared with matched controls (Enez Darcin et al., 2015; Fernandez-Egea et al., 2009; Garcia-Rizo et al., 2016; Kirkpatrick et al., 2009; Ryan et al., 2003). For example, in a recent study of a sample of 84 SCZ and 98 matched healthy subjects, the average glucose level in a 2-h glucose tolerance test was approximately 30% higher in SCZ patients (GarciaRizo et al., 2016). Second, T2D is more prevalent not only in SCZ patients but also in their families (Foley et al., 2016; van Welie et al., 2013; Yang et al., 2012). To summarize, the association between SMIs and T2D might result from an impairment of glucose metabolism or shared genetic risk factors and pathophysiological mechanisms. This review considers both of these possibilities.

2. ROLE OF GLUCOSE IN PHYSIOLOGICAL AND PATHOLOGICAL BRAIN FUNCTION Numerous metabolic disorders, such as homocysteine metabolism disorders, urea cycle disorders, lipid storage disorders, and leukodystrophies, are rare but important causes of psychiatric disorders in adolescents and adults (Demily and Sedel, 2014). Therefore, changes in metabolism can have serious consequences on the central nervous system. These effects can range from psychosis, depression, and mania in cases of severe disruptions to periodic or subtle behavioral disturbances in cases of mild disruptions. Glucose metabolism is closely integrated 6

with brain physiology, but unclear is whether dysregulated glucose metabolism can exert these kinds of effects.

2.1. Glucose energy metabolism in the brain The brain demands high amounts of energy. Although the brain represents only 2% of the mass of the human body, approximately 20% of the oxygen and 25% of the glucose that are consumed in the body are dedicated to brain function (Belanger et al., 2011). Two main processes contribute to the high energy demands of the brain: (i) maintenance and restoration of ion gradients that are dissipated by signaling processes, such as postsynaptic and action potentials, and (ii) the synthesis, uptake, and recycling of neurotransmitters (Alle et al., 2009; Harris et al., 2012; Mergenthaler et al., 2013). The overall energy budget of the brain is a result of intense coordination between the main cell types in the brain (i.e., neurons, astrocytes, and oligodendrocytes) and epithelial cells of cerebral blood vessels, which form the blood-brain barrier (BBB). The obligatory fuel for the brain is glucose. The brain requires a constant supply of glucose because it can store only a small amount of glycogen in astrocytes (Brown et al., 2005; Sickmann et al., 2009; Wender et al., 2000). Glucose is transported across the BBB through the saturable and insulin-independent glucose transporter 1 (GLUT1), which is expressed on vascular endothelial cells (Bondy et al., 1992; Kobayashi et al., 1996). The uptake of glucose by glial cells is also facilitated by GLUT1, whereas neurons use another insulin-independent transporter, GLUT3, which has higher affinity for glucose and higher transport capacity (Bondy et al., 1992). During maturation in childhood or upon prolonged starvation, brain cells can utilize ketone bodies that are produced in the liver (Lutas and Yellen, 2013). Different cell types in the brain have distinct metabolic profiles, which have been studied particularly for neurons and astrocytes (Belanger et al., 2011; Magistretti and Allaman, 2015) and are beginning to be elucidated for oligodendrocytes (Saab et al., 2013). Neurons sustain a high rate of oxidative metabolism compared with glial cells, which are characterized by a high rate of glycolysis (Harris et al., 2012). A large portion of glucose that enters the glycolytic pathway in astrocytes is released as lactate in the extracellular space and further used by neurons as an energy source. Recent evidence shows that lactate is also released by oligodendrocytes to feed 7

myelinated axons because myelin itself might limit the entry of glucose into the axonal compartment (Fünfschilling et al., 2012; Lee et al., 2012).

2.2. Effect of hyperglycemia and hypoglycemia on brain glucose metabolism The influx of glucose into the brain through the BBB by GLUT1 depends on the blood glucose concentration, which is strictly regulated by endocrine responses and varies between 3.9 and 6.1 mmol/L under physiological conditions (Falkowska et al., 2015). Hyper- and hypoglycemic clamp studies of conscious human subjects found that rising blood glucose levels were followed by a parallel increase in brain glucose (Abi-Saab et al., 2002; Heikkila et al., 2010; van de Ven et al., 2012). Notably, however, the level of glucose in extracellular fluid in the brain was always a few times lower than in blood and lagged approximately 30 min behind changes in plasma (Abi-Saab et al., 2002). The molar content of lactate levels in extracellular fluid in the brain was higher than in plasma and exceeded glucose levels under physiological conditions, reflecting the local generation of lactate from glucose by astrocytes (Belanger et al., 2011). Such a lag time, the local availability of lactate, and vascular barriers might play a protective role in limiting brain injury during severe hypo- or hyperglycemia. When blood glucose levels were acutely elevated to 11.5 mmol/L, the extracellular level in the brain was ~2.6 mmol/L (Abi-Saab et al., 2002). This indicates that cells in the brain might not be exposed to very high glucose levels during hyperglycemia. This contrasts with peripheral nerves, in which hyperglycemia is associated with marked increases in intracellular glucose (Stewart et al., 1966), and persistent hyperglycemia can lead to the development of diabetic neuropathy (Tomlinson and Gardiner, 2008). In studies of hypoglycemia, blood and brain levels dropped to 50% of baseline, while plasma lactate levels increased (AbiSaab et al., 2002). Many studies on diabetic rats reported a decrease in the transport of glucose into the brain during chronic hyperglycemia (Hou et al., 2007; Puchowicz et al., 2004), which is a possible adaptation to high glucose levels. If this also occurs in humans, then this could explain why diabetic patients develop transient symptoms of cerebral hypoglycemia after the rapid normalization of blood glucose levels (Bathla et al., 2014). The basis of this adaptation is unknown. A decrease in glucose transporters on epithelial cells in the BBB could also be an explanation, but no consensus has yet been reached in the literature (Hou et al., 2007; Shah and Parent, 2003). 8

Several studies of hypoglycemia in animals have reported an increase in GLUT expression in both the BBB and neurons (Kumagai et al., 1995; Simpson et al., 1999; Uehara et al., 1997). However, whether similar effects occur in humans is controversial (Boyle et al., 1994; Pelligrino et al., 1990; Segel et al., 2001). Further potential adaptation to lower glucose levels can involve the utilization of alternative energy substrates (e.g., ketone bodies or lactate) by increasing the level of monocarboxylate transporters (Herzog et al., 2013; Leino et al., 2001). In summary, the brain apparently attempts to adjust to recurrent and persistent hyper- or hypoglycemia in the long term.

2.3. Effects of acute hyperglycemia and hypoglycemia on cognition and mental states During ongoing hyperglycemia, diabetic patients often report acute cognitive and mental dysfunction, but only a few studies have confirmed such an association. In a study of 20 T2D patients (60 years old), impairments in learning and memory were observed during induced acute hyperglycemia (Sommerfield et al., 2004). In another study that simultaneously assessed fasting glucose levels and cognitive performance in diabetes patients approximately 50 times over 1 month, a positive correlation was found between acute hyperglycemia and mild cognitive impairment (Cox et al., 2005). The possible link between acute hyperglycemia and mental symptoms has been scarcely documented. Only a few studies have addressed this issue, and unequivocal conclusions have not been made. In one study, T2D patients (n = 20) experienced a lack of energy, sadness, and anxiety during acute hyperglycemia (glucose level = 16.5 mmol/L; (Sommerfield et al., 2004). In another small study (n = 15), patients reported an increase in well-being and less anger during mild hyperglycemia (10.5 mmol/L; (Pais et al., 2007). Hypoglycemia occurs in approximately 10-30% of T2D patients who are treated with insulin (McCrimmon and Sherwin, 2010). Hypoglycemia is moderate when blood glucose levels fall to 3.5-2.5 mmol/L and severe when blood glucose levels fall below 2.5 mmol/L (Languren et al., 2013). When blood glucose levels fall, compensatory neuroendocrine responses are initiated. If these defenses fail to abort the hypoglycemic episode, then a more intense sympathoadrenal response can cause so-called neuroglycopenic symptoms, including confusion, dizziness, difficulty speaking, blurred vision, irritability, fainting, seizures, or coma (Cryer, 2007; de Galan et al., 2006). 9

This is unsurprising when considering that neuronal function is energy demanding, and neurons depend on a regular supply of glucose. Severe hypoglycemia can lead to coma, characterized by unconsciousness and the cessation of electrical brain activity (isoelectric period), which might induce permanent neuronal damage, especially in the cortex, hippocampus, putamen, and caudate nucleus (Kalimo and Olsson, 1980; Ma et al., 2009; Shaefer et al., 2016). Nevertheless, brain damage is very rare, and recovery from hypoglycemia is usually complete after plasma glucose concentrations increase (1996).

2.4. Long-term effects of hyper- and hypoglycemia on cognition and brain integrity The effects of recurrent hyperglycemia on cognition are more pronounced than in the case of acute elevations of blood sugar. Long-term glycemic control (assessed by the glycohemoglobin test) in T2D patients is inversely related to performance on tasks that assess learning, reasoning, and memory. For example, a trial of the Action to Control Cardiovascular Risk in Diabetes– Memory in Diabetes (ACCORD-MIND) that included approximately 3,000 T2D patients found an association between cognitive decline and poor glycemic control (Cukierman-Yaffe et al., 2009). Importantly, however, the differences in cognitive function were mild to moderate. This cognitive decline was likely associated with hyperglycemia-related complications, such as brain atrophy associated with the presence of microvascular complications. Hyperglycemia is dangerous for cells because it causes oxidative stress and subsequent tissue deterioration (Tomlinson and Gardiner, 2008). One reason for these sequelae is that excess glucose is converted to sorbitol in the polyol pathway by utilizing NADPH as a reducer. Consequently, NADPH is depleted, similar to the key antioxidant glutathione, which also requires NADPH to be synthesized. This leads to the accumulation of reactive oxygen species, mitochondrial damage, and cell death. In parallel, sorbitol spontaneously interacts with proteins to produce advanced glycation end products, excess levels of which are harmful to cells in several ways, including cross-linking molecules and triggering inflammation (Ott et al., 2014). An oversupply of glucose is also metabolized in the hexosamine pathway, leading to the generation of N-acetylglucosamine. The subsequent glycosylation of proteins results in the dysregulation of signal transduction and transcription. Finally, very high extracellular glucose (> 30 mmol/L) increases osmolality, which pulls water out of cells and leads to dehydration. Because endothelial vascular cells are particularly exposed to hyperglycemia, diabetes-associated recurrent 10

hyperglycemia progressively compromises the vascular endothelium (Vinik and Flemmer, 2002). Failures in the micro- and macro-vasculature that are observed in the brain in T2D (Li et al., 2015) can lead to neurodegeneration in the brain (Tennant and Brown, 2013). This is probably the reason why different forms of dementia (e.g., vascular dementia, Alzheimer’s disease, and Parkinson’s disease) are more frequent in diabetic patients (Cheng et al., 2012; Cukierman et al., 2005; Gudala et al., 2013; Smolina et al., 2015; Wang et al., 2014). Human autopsy studies of patients who died from hypoglycemia showed multifocal and diffuse necrosis in the brain (Kodl and Seaquist, 2008). Unclear is whether recurrent hypoglycemia can cause any long-term deterioration of brain structures and permanent dysfunction. This issue has been addressed in only a few studies. The long-term consequences of recurrent hypoglycemia were recently assessed in the ACCORD-MIND magnetic resonance imaging (MRI) trial. This study included 500 T2D patients who were followed-up for 3 years (Zhang et al., 2014). Patients who experienced severe hypoglycemia during this follow-up period did not show more brain atrophy or white matter impairments than the other patients.

3. COMMON BIOLOGICAL RISK FACTORS UNDERLYING THE COMORBIDITY BETWEEN SERIOUS MENTAL ILLNESSES AND TYPE 2 DIABETES Glucose is essential to brain function, and many compensatory effects prevent or correct changes in physiological plasma glucose concentrations. The effects of hypo- or hyperglycemia on human cognition, discussed in the previous section, are quite well documented but indicate a lack of correlations, aside from those that are associated with vascular complications after chronic hyperglycemia. Therefore, a more plausible explanation for the comorbidity between T2D and SMIs are shared risk factors that independently contribute to these disorders.

3.1. Genetic risk factors Family history is an important risk factor for developing SMIs and diabetes, prompting scientists to search for genetic factors that are associated with these disorders. The possibility that T2D and SMIs may have a common genetic etiology was reinforced by GWASs. According to a widely accepted model, hundreds of common alleles confer a risk for diseases with a hereditary component, but each of these alleles alone has little effect (odds ratio > 1.5; (Ku et al., 2010). The 11

last decade has seen an explosion of GWASs, in which thousands of single-nucleotide polymorphisms (SNPs) are analyzed at the same time. The number of disease-associated genes is quickly growing. Considering that SCZ and T2D share some genetic vulnerability, an analysis of candidate genes for these disorders may shed light on possible common pathophysiology. The intersection of 268 SCZ susceptibility genes with 338 T2D susceptibility genes that were listed in the Genetic Association Database (http://geneticassociationdb.nih.gov) revealed a total of 37 common genes (Lin and Shuldiner, 2010) (Fig. 1). A similar procedure was applied to 200 SCZ genes that were listed in the Genetic Association Database and Catalog of Published Genome-Wide Association Studies and 196 T2D genes listed in the above database plus Type 2 Diabetes Association Database (Liu et al., 2013). In this study, 14 genes were found to intersect. Two functional categories could be identified within these groups: inflammation-associated genes (IL1B, IL6, IL10, HLA-A, HLA-DQA1, HLA-DQB1, HLADRB1, HSPA1B, TNF, CTLA4, APOE, and PTGS1) and genes that are involved in protecting cells from oxidative stress (PON1, SOD2, MTHFR, UCP2, and GSTM1). A network analysis based on interactions between protein products of all SCZ and T2D susceptibility genes and their interacting partners identified several hub proteins that are involved in calcium signaling, insulin signaling, adipokine signaling, and AKT signaling (Liu et al., 2013). Very recently, a genetic overlap between MD and T2D was identified based on data from DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) and Psychiatric Genomics Consortium meta-GWAS statistics (Ji et al., 2016). A functional analysis of the shared 496 SNPs revealed enrichment within pathways that are involved in immune responses, cell signaling (mitogen-activated protein kinase [MAPK], Wnt signaling), and lipid metabolism. To our knowledge, no analysis has been conducted concerning BD and T2D shared SNPs or genes. Currently, the National Human Genome Research Institute–European Bioinformatics Institute (NHGRI-EBI) catalog of published GWASs lists 402 SCZ susceptibility genes, 231 BD susceptibility genes, 257 depression susceptibility genes, 319 genes associated with SMIs (with no distinction between them), and 890 genes associated with T2D (calculated by the authors; https://www.ebi.ac.uk/gwas; accessed February 24, 2016). For the purpose of this review, we performed an analysis of the genes that are associated with mental disorders and T2D from the aforementioned NHGRI-EBI (Fig. 1). We found 79 candidate genes that are shared by T2D and any 12

of the SMIs. In this group, 26, 19, and 18 genes were specifically associated with T2D and SCZ, T2D and BD, and T2D and MD, respectively. Functional analysis of the group of 79 genes (DAVID database, https://david.ncifcrf.gov) revealed several clusters of common candidate risk genes. One cluster included genes that are involved in regulating multiple processes by intracellular signal transduction, such as genes that encode receptor regulators and ligand-binding proteins (ERRγ, NRG3, CDH13, FAF1, and APOB), components of signaling cascades (PRKCQ), and transcription factors and their regulators (NFKB1, TCF7L2, HNF1, ZMIZ1, and PPARGC1A). Some of these proteins have well-established roles in inflammation (NFKB1 and HLA-DQA1) or are involved in regulating the energy metabolism or action of insulin (HNF1, TCF7L2, CDH13, APOB, and SORBS1). Some genes that were identified in the intersection encode proteins that might have different functions in the nervous system and in peripheral organs (e.g., synapsin 2, transcription factor 7-like 2 [TCF7L2], and disrupted in schizophrenia 1 [DISC1]). Synapsin 2 is a synaptic protein that is involved in regulating neurotransmitter release from vesicles (Sudhof, 2004) and also expressed in β-cells, but its role in these cells is still unclear (Wendt et al., 2012). Below we present more details of the two other examples: the transcription factor TCF7L2 and multifunctional protein DISC1.

3.1.1. TCF7L2 The TCF7L2 gene encodes a transcription factor and effector of Wnt signaling. This signaling pathway is implicated in the pathophysiology and treatment of depression (Bordonaro, 2009; Niciu et al., 2013; Voleti and Duman, 2012) and was enriched among shared SNPs associated with both T2D and MD mentioned previously (Ji et al., 2016). TCF7L2 is the strongest genetic risk factor for T2D (Cauchi et al., 2007; Grant et al., 2006; Shen et al., 2015; Sladek et al., 2007; Welters and Kulkarni, 2008). Evidence from two independent cohorts showed an association between TCF7L2 and BD in obese patients (Cuellar-Barboza et al., 2016; Winham et al., 2014). Variants of TCF7L2 were linked with SCZ, but the statistical power was low in these studies (Alkelai et al., 2012; BenDavid et al., 2010; Hansen et al., 2011). Much research has sought to understand the mechanisms that underlie the metabolic function of TCF7L2 in organs that are involved in the pathogenesis of T2D, including the pancreas and liver. Accumulating evidence suggests that the activity of TCF7L2 is critical for the proliferation and survival of β-cells, modulation of insulin secretion (Liu and 13

Habener, 2010), and synthesis and processing of proinsulin and insulin (Zhou et al., 2014). The importance of TCF7L2 was also demonstrated in genetically modified mice. The disruption of Tcf7l2 in β-cells induced the development of glucose intolerance, β-cell dysfunction, a reduction of hepatic glucose production during fasting, and an improvement in glucose homeostasis when maintained on a high-fat diet (Boj et al., 2012; da Silva Xavier et al., 2012; Mitchell et al., 2015). Advances in our understanding of the role of TCF7L2 in organs that are involved in maintaining metabolic homeostasis have not been accompanied by similar analyses in the brain, where TCF7L2 is expressed at high levels in the medial habenula, thalamus, and inferior colliculus (i.e., areas that are implicated in sensory perception, attention, awareness, and decision-making; (Nagalski et al., 2013). Neuroimaging studies showed that these brain structures were affected in SCZ patients (Haijma et al., 2013; Kang et al., 2008; Shepard et al., 2006). The precise role of TCF7L2 in the brain has been enigmatic. Research from our group revealed the enrichment of TCF7L2 binding sites in regulatory regions of genes that are associated with neuronal excitability, such as genes that encode voltage-gated ion channels and neurotransmitter receptors, and transcription factors that are involved in the development of the thalamus (Nagalski et al., 2015; Wisniewska et al., 2010; Wisniewska et al., 2012). This suggests that TCF7L2 is an important regulator of neuronal excitability in the thalamus. The possible involvement of this (potential) function of TCF7L2 in pathophysiological mechanisms in SMIs needs further investigations.

3.1.2. DISC1 Another example of a SCZ-associated protein that could play separate roles in the brain and in the regulation of metabolism in the periphery is DISC1 (Soares et al., 2011). Although large cohort studies did not find an association between DISC1 and SCZ (Mathieson et al., 2012), mutations within this gene were found in members of large Scottish and American families who were affected by different SMIs (Farrell et al., 2015; Sachs et al., 2005). DISC1 acts as a scaffold protein. One of its interacting partners is glycogen synthase kinase 3/ (GSK3α/β), a mediator of the aforementioned Wnt pathway (Mao et al., 2009). Studies in genetically modified mouse models revealed multiple roles for DISC1 in the brain in neural precursor proliferation, neuronal migration, axonal guidance, dendritic growth, synaptic plasticity, and mitochondrial function and trafficking (Thomson et al., 2013). 14

Unexpectedly, a recent study showed that DISC1 is involved in regulating β-cell physiology in the pancreas (Jurczyk et al., 2016). In this study, the loss of DISC1 function in transgenic mice resulted in a decrease in β-cell proliferation, an increase in β-cell apoptosis, lower insulin secretion, and glucose intolerance. These effects were mediated by the inappropriate activation of GSK3, which was previously shown to play an important role in pancreatic β-cell function and survival (Mussmann et al., 2007).

3.2. Potential shared pathophysiology of serious mental illnesses and type 2 diabetes Accumulating evidence, including the aforementioned functional analyses of susceptibility genes, suggests that shared molecular and cellular mechanisms contribute to the development of SMIs and T2D. The following section focuses on insulin signaling, inflammation, mitochondrial dysfunction, oxidative stress, and neuroendocrine systems. Cross talk among these systems may be important in understanding the comorbidity between T2D and SMIs.

3.2.1. Insulin signaling in the brain Insulin was long considered a peripheral hormone that regulates energy metabolism in the liver, muscles, and fat. However, insulin receptors (INSRs) are widely distributed in the brain and expressed on both neurons and glial cells (Kleinridders et al., 2014; Werner and LeRoith, 2014). Furthermore, peripheral insulin crosses the BBB (Banks, 2004). There is a consensus that insulin acts in the brain, particularly in the hypothalamus, to regulate carbohydrate and lipid metabolism in the periphery and feeding behavior (Heni et al., 2015; Sanchez-Lasheras et al., 2010) (Fig. 2). For example, mice with neuron-specific deletion of the Insr gene (NIRKO mice) exhibited an imbalance in energy metabolism in the periphery and insulin resistance (Bruning et al., 2000), and insulin administration into the third ventricle in the brain in rodents suppressed glucose production in the body (Obici et al., 2002). Although the uptake of glucose by brain cells is generally insensitive to insulin, insulin likely regulates energy metabolism in a scattered population of neurons that express the insulinsensitive transporter GLUT4. A rodent model showed that GLUT4 colocalizes with INSRs in brain regions associated with motor control, the hippocampus, and the hypothalamus (El Messari et 15

al., 1998; Ren et al., 2014; Vannucci et al., 1998), forming a functional insulin-sensitive signaling pathway of glucose uptake. The role of this pathway in neurons is enigmatic, but it appears to regulate metabolism in these cells. For example, the local delivery of insulin in the rat hippocampus enhanced local glycolytic metabolism (McNay et al., 2010). Interestingly, this effect was attenuated in animals in which T2D was induced by a high-fat diet (McNay et al., 2010), suggesting that insulin resistance can also develop in the brain. A human study that used positron emission tomography and functional MRI found that insulin enhanced glucose uptake in regions with a high density of INSRs (Bingham et al., 2002; Kullmann et al., 2015). In neurons that express INSRs, similar to INSR-positive cells in the periphery, the activation of INSRs induces phosphoinositide 3-kinase (PI3K)/AKT signaling via INSR substrates. This cascade targets multiple downstream pathways (e.g., mTOR, ERK/MAPK, GSK3α/β or FOXO pathways), which in the case of neurons are implicated in the regulation of cell survival and synaptic plasticity (Adams and Sweatt, 2002; Kim et al., 2012; Ren et al., 2013; Salcedo-Tello et al., 2011; Stoica et al., 2011). Therefore, insulin in the brain can exert general neuroprotective and trophic effects and improve learning and memory. Such an assertion was corroborated by animal studies (Fanne et al., 2011; Grillo et al., 2015; Sanderson et al., 2009) and human studies with intranasally administered insulin (Ott et al., 2012). Insulin was also proposed to target dopaminergic systems that regulate reward-motivated behavior and play a crucial role in SMIs. In a mouse model, insulin had a significant excitatory effect in a major subpopulation of dopaminergic neurons in the basal ganglia, and this effect was abolished by knockout of the Insr gene (Konner et al., 2011). Furthermore, NIRKO mice exhibited depressive-like behavior (Haider et al., 2013; Kleinridders et al., 2015; Sharma et al., 2010). Unknown is the way in which insulin mechanistically contributes to the regulation of dopaminergic systems. Nevertheless, insulin treatment in streptozotocin-induced diabetic mice showed that insulin decreased monoamine oxidase (i.e., the enzyme that is responsible for the degradation of dopamine) activity in the brain (Gupta et al., 2014). Converging evidence indicates that insulin, apart from regulating peripheral glucose metabolism, affects a wide range of normal brain functions, such as memory formation and reward circuit, and these functions can be altered in T2D, potentially contributing to cognitive or mental impairments. 16

3.2.2. Inflammation Many inflammation-associated genes have been identified as genetic risk factors for the development of SMIs and T2D. Obesity, insulin resistance, and T2D are closely associated with inflammation (Hotamisligil, 2006; Pradhan et al., 2001). An imbalance in the cytokine/chemokine network is also involved in SMIs (Landek-Salgado et al., 2016). Tumor necrosis factor α (TNFα), interleukin 1β (IL-1β), and IL-6 levels were shown to increase in murine models of obesity and T2D (McArdle et al., 2013). TNFα is overexpressed in adipose and muscle tissue in obese humans, and exogenous TNFα administration leads to insulin resistance (Hotamisligil et al., 1995; Kern et al., 1995; Saghizadeh et al., 1996). The secretion of TNFα, IL-1β, and IL-6 draws immune cells to adipose tissue and initiates the inflammatory response. The activation of TNFα signaling pathways impairs insulin signaling at the level of INSR substrates through inhibitory phosphorylation (Lorenzo et al., 2008). The disruption of adipose tissue function causes further defects in hepatic and skeletal muscle glucose homeostasis, resulting in systemic insulin resistance and leading to the development of T2D (McArdle et al., 2013). It is generally accepted that maternal infection during pregnancy and multiple infections in childhood increase the risk of SCZ (Brown, 2011), and the reason for it can be an effect of inflammation on brain development. Nevertheless, inflammatory cytokines are elevated also in adult SMI patients. A recent meta-analysis found that serum levels of IL-1β, IL-6, TNFα, and serum IL-2 receptor were increased in medication-naive patients with first-episode psychosis and in BD patients (Bauer et al., 2014; Upthegrove et al., 2014). A meta-analysis of MD confirmed higher mean levels of IL-6 and C-reactive protein but not TNFα or IL-1β (Haapakoski et al., 2015). Evidence indicates that proinflammatory cytokines have a negative effect on brain-derived neurotrophic factor (BDNF) expression levels in patients with affective disorders (Grassi-Oliveira et al., 2008; Hayley et al., 2005; Kauer-Sant'Anna et al., 2007; Mondelli et al., 2011). Polymorphisms within the BDNF gene were associated with SCZ (Bonaccorso et al., 2015; Li et al., 2012; Li et al., 2013; Watanabe et al., 2013), mood disorders (Bonaccorso et al., 2015), and obesity (Beckers et al., 2008; Ernst et al., 2012; Gray et al., 2006; Hotta et al., 2009), although large cohort studies failed to confirm this association. Moreover, mice with deficient BDNF expression exhibited behavioral abnormalities, such as aggression and hyperactivity (Coppola and Tessarollo, 17

2004), and also obesity (Sha et al., 2007). Therefore, inflammation-associated alterations in BDNF expression could be another mechanism of the development of SMIs and diabetes. A GWAS that included nearly 40,000 SCZ and 120,000 control subjects corroborated a strong association between SCZ and genes that are implicated in the immune response, particularly the major histocompatibility complex (MHC) locus (Schizophrenia Working Group of the Psychiatric Genomics, 2014). A study that used a mouse model found that variations in the complement component 4 (C4) gene within this locus affected C4 expression in the brain, and C4 in synapses was critically involved in synapse elimination by microglia during postnatal development (Schafer et al., 2012; Sekar et al., 2016). This mechanism may link immune response-associated alterations with synaptic pruning in SCZ (Boksa, 2012). There is strong genetic and pathophysiological evidence of a role for the inflammatory response and MHC complex in the development of SMIs and TD2. These associations can contribute to the comorbidity between these disorders.

3.2.3. Mitochondria The implication of mitochondrial dysfunction in the development of insulin resistance is still debated (Montgomery and Turner, 2015). Although reductions of mitochondrial content, alterations of the expression of subsets of mitochondrial genes, and decreases in the expression of mitochondrial oxidative proteins have been well described in the pathogenesis of T2D, various independent studies have failed to show such a correlation, as reviewed elsewhere (Kaufman et al., 2015; Martin and McGee, 2014; Montgomery and Turner, 2015). Several lines of evidence suggest that mitochondrial dysfunction is an important component in the pathology of SMIs, especially BD and SCZ (Manji et al., 2012). Postmortem examinations of brain tissue ultrastructure and metabolomic studies have provided evidence of mitochondrial dysfunction in the brain in SCZ and BD patients (Clay et al., 2011). Perturbations in mitochondrial morphology and a decrease in the number of mitochondria were observed in different brain regions in qualitative electron microscopy studies of SCZ brains (Kolomeets and Uranova, 2010; Kung and Roberts, 1999; Roberts et al., 2015; Uranova et al., 2011; Vikhreva et al., 2016). Decreases in the activity of the mitochondrial respiratory chain complexes were also reported in 18

different brain areas in SCZ and BD patients (Andreazza et al., 2010; Maurer et al., 2001). Largescale transcriptomic and proteomic studies further supported the mitochondrial hypothesis of SMIs by showing decreases in the gene/protein expression of Krebs cycle and/or respiratory chain enzymes in SCZ and BD brains (Altar et al., 2005; Gottschalk et al., 2015; Iwamoto et al., 2005; Konradi et al., 2004; Middleton et al., 2002; Pennington et al., 2008; Prabakaran et al., 2004), mostly in the prefrontal cortex. However, these results were only partially consistent with each other. Although some of the studies reported decreases in both SCZ and BD samples, others reported a decrease only in SCZ samples or only in BD samples. Moreover, one study reported that the expression of the aforementioned genes actually increased in BD samples (Gottschalk et al., 2015). Another question is whether mitochondrial dysfunction is a cause or consequence of T2D and SMIs. The former is corroborated by numerous data that showed that mitochondrial genes are impacted by copy number variations, polymorphisms, and mutational events in SCZ, BD, and MD (Bi et al., 2016; Purcell et al., 2014; Rollins et al., 2009; Sequeira et al., 2012; Sequeira et al., 2015). Variations in mitochondrial DNA and mitochondria -associated genes were also reported to be associated with the risk of developing diabetes (Cho et al., 2007; Kwak and Park, 2016). Another line of evidence comes from case studies that reported the prevalence of various psychiatric symptoms in patients with recognized mitochondrial diseases (Fattal et al., 2006; Inczedy-Farkas et al., 2012). However, animal studies, in which the function of mitochondria was compromised, did not confirm the causative involvement of mitochondria in the development of insulin resistance (Montgomery and Turner, 2015). With the cumulative evidence of alterations in mitochondrial function in both SMIs and T2D, there is limited evidence that mitochondrial dysfunction is an etiological factor in these disorders.

3.2.4. Oxidative stress Inflammation and mitochondrial dysfunction are interconnected with oxidative stress, which occurs when antioxidant defense mechanisms fail to counterbalance reactive oxygen species. Recent meta-analyses revealed that markers of oxidative stress increase in SCZ (Emiliani et al., 2014; Flatow et al., 2013), BD (Brown et al., 2014), and MD (Black et al., 2015). Imbalances in oxidative and antioxidant defense systems have also been associated with T2D (Maiese, 2015). 19

An interesting link between genetic associations and oxidative stress in these disorders was provided by methylenetetrahydrofolate reductase (MTHFR), an enzyme that contributes to homocysteine metabolism. MTHFR gene was associated both with T2D and SCZ. Elevated levels of homocysteine may lead to an increase in the generation of superoxide, which is further amplified by homocysteine-dependent alterations in the function of cellular antioxidant enzymes (Weiss et al., 2003). Many other genes that are implicated in antioxidant defense systems have been linked to T2D (Banerjee and Vats, 2014), SCZ (Chowdari et al., 2011), and BD (Fullerton et al., 2010). The correlation between oxidative stress, T2D, and SMIs appears to be well established.

3.2.5. Neuroendocrine system Hypothalamic-pituitary-adrenal (HPA) axis is a major neuroendocrine system that control reaction to stress, and regulates, among others, processes of energy storage and expenditures, immune functions, mood and emotions. It is not surprising that dysfunctions of HPA axis is suggested as a pathological factor in SMIs and T2D (Chan et al., 2003; Keller et al., 2006; Lamers et al., 2013; Pariante and Lightman, 2008; Steen et al., 2014) The elevated systemic cortisol metabolism is present in both SCZ (Steen et al., 2014; Steen et al., 2011) and BP (Cervantes et al., 2001; Daban et al., 2005; Watson et al., 2004). The hyperactivity of the HPA axis and hypercotisolemia is one of the most consistent biological findings in MD (Pariante and Lightman, 2008; Stetler and Miller, 2011). Also, there is growing evidence that in humans prenatal stress influences HPA regulation and is associated with higher prevalence of mental illnesses including SCZ and depression (Kim et al., 2015). Insulin resistant and obese individuals exhibit elevated cortisol that is produced by the adrenal cortex (Chiodini et al., 2007). Hypercortisolaemia may increase adiposity; therefore contribute to metabolic syndrome, insulin resistance, and T2D (Adam and Epel, 2007; Gibson, 2012). This effect is mediated by an influence of cortisol on the secretion of leptin (secreted by adipose cells), insulin and brain neuropeptide Y (NPY), which are both abovementioned risk factors for T2D and SMIs. Insulin and NPY, which both act on hypothalamic neurons, are involved in the regulation of appetite. They also influence neurons in the dopaminergic reward circuit (described for insulin hereinbefore). It was recently proposed that another neuroendocrine system, the dopamine-prolactin pathway contribute to the association between T2D and SCZ (Gragnoli et al., 2016). Dopamine 20

that is released to the pituitary gland acts as a prolactin-inhibitory factor. The levels of both dopamine and prolactin appear to be dysregulated in SCZ (Garcia-Rizo et al., 2012). Prolactin is essential for pancreatic development and insulin secretion capability (Auffret et al., 2013; Freemark et al., 2002; Huang et al., 2009; Terra et al., 2011). Dysregulation of the dopamineprolactin pathway may impair glucose homeostasis and thus contribute to the higher incidence of T2D in SCZ patients. However, this hypothesis needs further investigation because dopamine and prolactin levels were found to both increase and decrease in SCZ patients (Albayrak et al., 2014; Garcia-Rizo et al., 2012; Howes and Kapur, 2009; Jose et al., 2015). Additionally, NPY is of interest to the hypothesized correlation of the dopamine–prolactin pathway with the SMI comorbidity, as NPY appears to amplify the inhibitory action of dopamine on prolactin secretion (Wang et al., 1996). Accumulating evidence suggesting a link between dysregulation of HPA axis and dopamineprolactin pathways and the risk of developing SMIs and T2D. NPY might be an important player in this association.

4.

CONCLUSIONS The comorbidity between T2D and SMIs is unquestionable. Less certain is whether biological

links between these disorders per se are contributing factors. The long-term consequences of hyperglycemia, which is the principal diabetic pathology, appear to contribute to cognitive decline and neurodegeneration, but is rather a result of vascular complications. Instead, accumulating evidence suggests that the pathogenesis of diabetes and SMIs includes shared molecular and cellular mechanisms. Candidate risk genes for mental and metabolic illnesses that have been revealed by recent large-scale GWASs, together with neuroimaging and animal studies, can help identify these mechanisms. The concept of systemic disorders in SMIs has been proposed in the literature on BD, SCZ, and MD (Berk et al., 2013; Garcia-Rizo et al., 2015; Kirkpatrick and Miller, 2013), mostly based on abnormal neuroendocrine status. Systemic inflammation and oxidative stress appear to be other possible and well-documented mediators of SMIs. There is also an emerging role for insulin in the pathophysiology of SMIs. We may conclude that SMIs could be at least partially considered metabolic and systemic disorders. We believe that the development of future therapeutic approaches for the treatment of psychiatric illnesses will rely on identifying 21

their different molecular and cellular endophenotypes, such as those that are associated with T2D, to develop novel personalized treatments that are more safe and effective than those that are currently used. ACKNOWLEDGEMENTS This work was supported by Polish National Science Centre grant 2015/19/B/NZ4/03571.

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FIGURE LEGENDS Fig. 1. Intersection of susceptibility genes for schizophrenia (SCZ), type 2 diabetes (T2D), and serious mental illnesses (SMIs) revealed in genome-wide association studies (GWASs). Numbers in circles indicate the number of genes associated with the risk of a particular disorder.

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Fig. 2. Potential roles of insulin in the brain.

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