Prior to the APD era, cohort studies (Kooy, 1919; Kasanin, 1926) noted increased incidence of abnormal glucose metabolism in people with schizophrenia (Henneman et al., 1954). These observations corroborated cross-sectional results demonstrating that the prevalence of diabetes was greater in patients with schizophrenia compared to the general population (Kasanin, 1926). Examination of the data in pre-APD studies of schizophrenia patients suggests that abnormal glucose metabolism far preceded the confounding effects of pharmacologic intervention. Interestingly, a study in 1922 determined that increased fasting and post-prandial blood glucose levels in untreated patients with schizophrenia was in part correlated to the severity of their illness. Catatonic patients experienced more than twice the maximum post-prandial plasma levels of glucose than disorganized schizophrenia patients. Moreover, the fasting glucose level after a 12-h fast was uniformly higher in catatonic patients than in those with other forms of schizophrenia (Lorenz, 1922).

Yet, there is a wide heterogeneity among the metabolic sequelae associated with schizophrenia across all stages of the illness. In the literature preceding the era of APD treatment, Kasanin (1926) reported on a case series of 154 people with dementia praecox (later renamed schizophrenia), stating: “From this compilation it is evident that we could find no curve that is characteristic of the “dementia praecox” group as a whole. This is not unexpected in view of the heterogeneous character of the clinical conditions described under this head” (Kasanin, 1926). Further, even for those taking medications with the highest metabolic risk such as, olanzapine and clozapine, there is a notable percentage, more than 15%, that do not gain weight (Leadbetter et al., 1992). Rather, these medications may still produce significant metabolic disturbances including hyperglycemia and dyslipidemia that are independent of effects on body mass (Kang and Lee, 2015). While such wide-ranging effects make the study of a definitive mechanism more challenging, they nevertheless demonstrate the likely existence of a metabolic phenotype intrinsic to schizophrenia. Moreover, there is accumulating evidence suggesting the likelihood of multiple pathways responsible for the diversity of metabolic phenotypes in people with schizophrenia.

More recent studies have confirmed the pre-medication era findings, and have continued to show impaired glucose tolerance, dyslipidemia, and related aspects of metabolic syndrome in drug-naïve patients with schizophrenia, as compared to healthy controls (Fernandez-Egea et al., 2009; Enez Darcin et al., 2015). In a 2016 case-control study of drug-naïve patients with schizophrenia, almost one-fourth of patients had baseline impaired glucose tolerance compared to none of the controls. The patients with schizophrenia also had higher fasting glucose levels and greater IR. However, compared to the controls, the patients with schizophrenia had higher BMIs, LDL levels, TG levels, waist circumference, and hip circumference. It is unclear to what extent those factors confounded the impaired glucose tolerance observed in patients with schizophrenia, though it is notable that these anthropometric parameters differed between cases and controls before the opportunity for pharmacologic intervention (Chen et al., 2016). Similar findings were found in youth through the recent Tolerability and Efficacy of Antipsychotics (TEA) trial. Drug-naïve first-episode psychosis patients ages 12–17 years had higher waist circumference, total cholesterol, LDL, and non-HDL levels compared to controls (Jensen et al., 2017). These suggestions have been challenged in meta-analyses that exclude patients with affective psychosis (which could lead to confounding changes in dietary or exercise habits). One meta-analysis found that the difference in LDL levels between first-episode, non-affective psychosis patients and controls was clinically insignificant. The systemic review did, however, find that first-episode, non-affective psychosis patients had higher triglyceride levels, and lower HDL levels than the controls. The study suggests that this may represent subclinical dyslipidemia, and hypothesizes that psychosis-specific mechanisms may be at play, perhaps by sharing overlapping genetic regions with metabolic phenotypes (Misiak et al., 2017).

Another case-control of drug-naïve, first-episode patients with psychosis showed higher insulin and C-peptide levels and lower HDL levels compared to the age- and BMI-matched controls. This suggests that even when accounting for the possible effect that schizophrenia itself may have on BMI, IR is still increased in patients with psychosis compared to healthy controls. Interestingly, when controlling for BMI, patients and controls were no different in their triglyceride, total cholesterol, and fasting glucose levels (Petrikis et al., 2015). Indeed, use of APDs certainly increases the risk of metabolic disturbance, but as discussed, there is a baseline elevated risk for metabolic syndrome in people with schizophrenia prior to antipsychotic use. Consistent with this, a meta-analysis of 48 studies showed a 10.2% prevalence of metabolic syndrome in antipsychotic-naïve patients, but a rate of 19.4% for patients on aripiprazole, and 47.2% for patients on clozapine. This further suggests that the disease state has an independent influence on adverse metabolic symptoms beyond the treatment-induced effect (Vancampfort et al., 2015; see Table 1).

In addition to individual smaller studies, two large meta-analyses of studies of first-episode and drug-naïve schizophrenia patients further showed a baseline increased risk for metabolic syndrome at the onset of treatment, which only worsened over progression of the illness (Mitchell et al., 2013). Glucose homeostasis was impaired from the onset of schizophrenia, despite patients having similar hemoglobin A1c levels relative to controls. The factors that were higher in patients included fasting plasma glucose levels, glucose levels after an oral glucose tolerance test, fasting insulin levels, and IR. The meta-analysis concluded that patients with schizophrenia were at an increased risk of diabetes, only exacerbated by the effects of chronic illness and long-term treatment (Pillinger et al., 2017).

A population-based study in Denmark tested the added risk of starting APDs. While APD-naïve patients with schizophrenia had a higher rate of diabetes by a factor of 3.07 compared with the general population, the risk of diabetes was 3.64 times higher in patients started on APDs compared to patients with schizophrenia who remained APD-naïve. Both first-generation and second-generation APDs increased this risk more than three-fold, with no statistically significant between the two drug classes in their capacity to cause these metabolic sequelae (Rajkumar et al., 2017).

Impaired glucose tolerance has also been demonstrated in non-psychotic, first-degree relatives of schizophrenia patients, further indicating a heritable phenotype that tracks with risk of psychosis, but is independent of the actual development of a psychotic disorder (Spelman et al., 2007). This lends credence to the concept of a novel metabolic endophenotype associated with schizophrenia with variable penetrance across both affected individuals and their unaffected relatives.

Significantly, the risk of metabolic abnormalities further increases significantly with duration of illness. Chronically-ill subjects exhibit increased rates of metabolic dysfunction compared to first-episode and drug-naïve patients (Mitchell et al., 2013; Correll et al., 2014). Additional lifestyle factors including sedentary lifestyle, poor diet, and increased smoking further add to the metabolic risks associated with schizophrenia (Brown et al., 1999).

In addition to the intrinsic risk factors for metabolic disease associated with schizophrenia including genetics and lifestyle (Ballon et al., 2014; Heald et al., 2017; Rado, 2017), there is a consensus that APDs can further exacerbate these metabolic disturbances (Kane et al., 2004). Many APDs currently available in the United States have been associated with metabolic side effects to varying degrees (Kendall, 2011). Clozapine and olanzapine, though the most clinically effective APDs available, have also shown the greatest risk for inducing IR and significantly elevate risks for major cardiovascular events (e.g., acute coronary syndrome, ischemic stroke, and peripheral artery disease) by up to 2.8-fold (Szmulewicz et al., 2017). Indeed, 32% of patients taking olanzapine develop IR, in addition to gaining at least 15% of their baseline bodyweight (Citrome et al., 2011). Such APD-induced IR is a critical factor in the increased risk for coronary vascular disease, and thus is one of the principal causes of morbidity and premature mortality in this population (Hennekens et al., 2005; Laursen et al., 2012). Other APDs such as, ziprasidone and lurasidone have lesser cardiometabolic risks (Allison et al., 1999), though still elevate risk for other metabolic abnormalities such as, non-alcoholic fatty liver disease (Morlan-Coarasa et al., 2016).

Significantly, while much of the field's focus on metabolic side effects has been on newer medications (second-generation or atypical APDs), even first-generation APDs, such as, haloperidol or chlorpromazine, have been associated with weight gain and IR (Gordon et al., 1960; Fleischhacker et al., 2012). Consistent with this, in the large EUFEST trial (European First Episode Schizophrenia Trial), while olanzapine produced a 13.9 kg weight gain in subjects compared to baseline, haloperidol still caused a significant 7.3 kg weight gain as well (Kahn et al., 2008; Fleischhacker et al., 2012). Similarly, the “Evaluation of METabolic disordErs in schizOphRenic patients” (METEOR) study found no significant differences between patients prescribed first- or second-generation APDs in terms of the prevalence of glycemic disorders, dyslipidemia, and metabolic disorder (Falissard et al., 2011). These findings argue that most, if not all, APDs may cause or exacerbate metabolic symptoms and associated morbidity (Kahn et al., 2008). Data from the CATIE (Clinical Antipsychotic Trials of Intervention Effectiveness) study also showed substantial effects on metabolic in subjects taking APDs (McEvoy et al., 2005). When compared with a matched sample of people without psychiatric illness from the National Health and Nutrition Examination Survey (NHANES), people with schizophrenia treated with APDs had the highest metabolic risk of any patient group (McEvoy et al., 2005). These results reaffirm that, independent of medication effects, schizophrenia appears to confer an intrinsic risk of metabolic dysfunction, and APD treatment exacerbates this preexisting susceptibility to metabolic disease (Spelman et al., 2007). Therefore, the combination of inherent metabolic risks, both genetics and lifestyle, when combined with APD treatment, leads to the greatest possible metabolic risk category in medicine.

Most studies examining either APD-induced metabolic side effects or intrinsic metabolic risk in schizophrenia have focused primarily on regions of the central nervous system (CNS) associated with metabolic control (e.g., hypothalamus). Consequently, numerous neurotransmitter and neuropeptide systems in the brain have been implicated in mediating these metabolic phenomena including the monoamines dopamine, serotonin, and histamine (Nasrallah, 2008).

Weight gain is fundamentally due to an energy imbalance where there is a surplus of energy intake over energy consumption. This leads to storage of the excess energy typically leading to increased weight (Muller and Kennedy, 2006). Most commonly, these increases in energy intake are due to increased caloric intake secondary to enhanced appetite. In addition to the above evidence for APD actions on the histamine system in causing appetite changes that lead to obesity (Deng, 2013), second-generation APDs also act on several neuropeptides that modulate appetite and food intake. For example, olanzapine and risperidone have both been shown to increase levels of appetite-stimulating neuropeptides including neuropeptide Y (NPY) and agouti-related peptide (AgRP), along with elevations in H1R expression to further potentiate these effects (Lian et al., 2015). Olanzapine may also increase appetite through actions on ghrelin, an orexigenic peptide, where ghrelin receptor signaling is enhanced by the drug (Tagami et al., 2016). Likewise, APDs may further increase appetite by decreasing levels of proopiomelanocortin (POMC), an appetite-inhibiting neuropeptide (Lian et al., 2015). Importantly, POMC and its precursor pre-POMC are differentially processed to yield several other metabolically-relevant derivative molecules including β-endorphin, melanocyte-stimulating hormones (MSHs), and adrenocorticotropic hormone (ACTH; Millington, 2007; Mountjoy, 2015; Anderson et al., 2016). Recent work suggests that one of these derivatives, α-MSH, may play a role in appetite regulation (Vehapoglu et al., 2016). Consequently, decreased levels of α-MSH, an anorexigenic hormone, in response to risperidone treatment likely further contribute to the increases in appetite observed clinically (Baltatzi et al., 2008; Yanik et al., 2013). Evidence also suggests that one of the MSH receptors, melanocortin 4 receptor (MC4R), is strongly linked with both weight regulation and APD-induced obesity (Zhang et al., 2016). Indeed, MC4R function is implicated in energy expenditure as well as regulation of food intake independently of changes in body weight (Xu et al., 2013; Mountjoy, 2015). Furthermore, a single nucleotide polymorphism (SNP) in MC4R, rs17782313, has been associated with overeating as well as APD-induced weight gain (Yilmaz et al., 2015; Macneil and Muller, 2016). Two additional SNPs, rs8087522, and rs1801133, have also been linked to APD-induced weight gain (Malhotra et al., 2012; Macneil and Muller, 2016), suggesting that the MC4R locus may be an attractive candidate for predicting APD-induced weight gain and metabolic disruption.

In terms of energy expenditure, APDs such as olanzapine reduce locomotor activity in rodent models, which further disrupts the balance between energy intake and consumption and leads to weight gain (van der Zwaal et al., 2014). Moreover, these olanzapine-induced decreases in locomotor activity occur at doses that do not affect eating behavior (Weston-Green et al., 2011). It has been suggested that APDs' sedative properties play an important role in causing this diminished energy consumption (van der Zwaal et al., 2014). Consistent with this, there is significant incidence of somnolence in people treated with many second-generation APDs (Gao et al., 2008). Furthermore, thermogenesis is another measure of body energy consumption and olanzapine administration was shown to cause reductions in body core temperature in animal model (van der Zwaal et al., 2012, 2014). Therefore, these drug-induced decreases in body core temperature suggest overall decreases in energy expenditure. Nevertheless, it is unclear to date if this is the case. Several trials have shown either no significant effect of APDs on resting energy expenditure (REE; Graham et al., 2005; Vidarsdottir et al., 2010), decreased REE (Sharpe et al., 2005, 2006; Nilsson et al., 2006) or even elevated REE (Fountaine et al., 2010). Intriguingly, unmedicated people with schizophrenia exhibited decreased REE (Nilsson et al., 2006). Overall, these studies suggest that the relationships between changes in thermogenesis and REE are likely complex and APD effects on body core temperature may cause compensatory changes in REE.

The ability of APDs to differentially target the various respective receptor signaling systems may have profound effects on both appetite and energy balance. Differences in the respective APDs' actions on both arms of energy balance regulation is likely responsible both for differences in the magnitude of significant metabolic side effects caused by these medications. However, this is complicated clinically by the observation that there is relatively high overall variability in the phenotypes of APD-induced metabolic dysfunction. Of the people who develop metabolic side effects, some gain weight with no IR; some develop IR without weight gain, and some experience both. Based on the discrepancy between weight gain and IR, an alternate treatment approach has been to focus on effects of APDs on appetite (Mizuno et al., 2014). Therapeutically, this has led to studies of appetite suppressants like topiramate and dextroamphetamine, to target CNS appetite centers. Nevertheless, appetite suppressants, including topiramate, have little to no effect in treating either intrinsic or APD-induced metabolic dysfunction, and cause significant cognitive and/or psychotic side effects that limit their overall safety (Narula et al., 2010; Muscatello et al., 2011; Mizuno et al., 2014). This highlights the conundrum concerning the roles of monoamine signaling in schizophrenia's intrinsic effects on metabolism and APD-induced metabolic effects: despite significant strides in understanding the physiology and pharmacology of monoamine signaling in the CNS, there has been a paucity of major mechanistic breakthroughs or new treatments that have mitigated these metabolic disturbances (Mizuno et al., 2014).

A potential explanation for the limited efficacy of drugs targeting APD-induced metabolic dysfunction is their focus primarily on CNS targets. Increasing evidence suggests that the same molecular targets of APDs, such as, histamine, serotonin, and dopamine receptors, also exist in peripheral organs critical for metabolic control, including the pancreas, adipose tissue, and skeletal muscle (Garcia-Tornadu et al., 2010; Rubi and Maechler, 2010; Ballon et al., 2014; see Figure 1). These peripheral targets, together with the CNS, jointly regulate both body weight and glucose/insulin homeostasis, key factors in APD-induced metabolic dysfunction (Ballon et al., 2014). For example, insulin-secreting pancreatic beta cells not only produce their own dopamine, but also express D2R, an important APD target (Rubi et al., 2005; Simpson et al., 2012). There is further precedent for CNS neurotransmitter action in the periphery, including in the gastrointestinal tract and adipose tissue (Gershon, 2013). Moreover, there is crosstalk between the CNS and these peripheral organs since hypothalamic and brain stem neurons have been shown to influence beta cell, hepatocyte and adipocyte metabolism (Elmquist et al., 2005) and vice versa.

Glucose and insulin-sensitive tissues in the periphery, including pancreatic beta cells and adipose cells, express D₂-like receptors (D2R, D3R, and D4R), which are key targets of APDs (Wilson et al., 1998). In addition, insulin-secreting pancreatic beta cells also express the CNS-specific isoform of the vesicular monoamine transporter, VMAT2, which is responsible for vesicular dopamine loading and storage (Anlauf et al., 2003). We have shown that dopamine acts as an autocrine or paracrine negative regulator of glucose-stimulated insulin secretion (GSIS) to dampen further insulin release (Figure 2A; Simpson et al., 2012). Moreover, we have also demonstrated that the addition of sulpiride, one of the most D2R/D3R-selective APDs (Newman et al., 2012), increases GSIS by 40% in pancreatic beta cells. Importantly, this APD-induced increase in insulin secretion is consistent with the chronic hyperinsulinemia found in APD-induced IR clinically (Henderson et al., 2005). Conversely, D2R and D3R agonism may have therapeutic effects on IR. Indeed, a quick-release formulation of the D2R/D3R agonist bromocriptine is FDA-approved to treat T2D (Mikhail, 2011; Lamos et al., 2016). Although the majority of work studying bromocriptine's metabolic effects has focused on its actions in the CNS, there is robust evidence that bromocriptine also targets the same peripheral dopamine signaling pathways altered by APDs, as described above (Holt et al., 2010; Farino et al., 2016; Lamos et al., 2016). Bromocriptine lowers both elevated glucose and insulin levels in humans (Liang et al., 1998; Holt et al., 2010). Based on this evidence, bromocriptine may be effective in treating APD-associated IR by targeting peripheral dopamine pathways disrupted by APDs. Consistent with this, data from small studies is validating this hypothesis, having demonstrated reestablishment of euglycemia in the context of APD treatment (Naguy and Al-Tajali, 2016). On a cellular level, we have now demonstrated that bromocriptine treatment diminishes GSIS comparably to dopamine in a dose-dependent manner in a beta cell-derived cell line (Figure 2B). These data suggest that dopamine D₂-like receptors may play an important role in the periphery to modulate secreted insulin. Furthermore, the disruption of this regulatory signaling either through genetic changes intrinsic to the disease process in schizophrenia or via drug action by APDs may lead to many of the metabolic disturbances observed clinically.

Given the importance of peripheral metabolic targets, initial attempts at treating APD-associated IR focused on current T2D medications already known to act in the periphery (Mizuno et al., 2014). The widely-used class of T2D drugs, thiazolidinediones, increase insulin sensitivity (IS) in T2D by acting on peripheral targets including adipose tissue, skeletal muscle, and liver to increase glucose utilization and decrease glucose production (Hauner, 2002). Besides T2D, these drugs have been applied to treatment of metabolic dysfunction associated with psychiatric disorders. However, when rosiglitazone was studied in people with schizophrenia on clozapine, it was ineffective (Henderson et al., 2009). Given these results with thiazolidinediones, further trials looked to other classes of insulin sensitizing drugs. Metformin, a biguanide, improves blood glucose levels in T2D through decreased hepatic glucose production and increased peripheral glucose utilization (Zhou et al., 2001). Though metformin modestly decreases APD-induced weight gain, like the thiazolidinediones, it has also failed to meet expectations for improving APD-induced IR (Jarskog et al., 2013; Rado and Von Ammon Cavanaugh, 2016). Based on the relative failure for using these otherwise well-established mechanistic strategies to treat IR in the setting of ongoing APD treatment, a new therapeutic strategy is clearly needed. To date, such attempts have been hampered by the relative paucity of knowledge regarding the biological modulators of metabolic dysfunction both in the cases of intrinsic and APD-induced metabolic dysfunction. Below we will review the roles of several such modulators.

Adiponectin is an adipokine hormone, produced primarily by fat cells in adipose tissue that makes tissues more sensitive to insulin (Diez and Iglesias, 2003). Adiponectin levels are inversely related to obesity (Di Chiara et al., 2012). Further, low levels of circulating adiponectin are associated with IR and may provide a link between obesity and overall metabolic syndrome (Di Chiara et al., 2012). Increasing adiponectin leads to improvements in insulin sensitivity (IS) through enhanced tissue fat oxidation in adipose tissue, lowering circulating fatty acid levels and reducing intracellular triglyceride contents in liver and muscle (Diez and Iglesias, 2003). It further lowers inflammation and thus reduces risk of atherosclerosis, by suppressing the expression of adhesion molecules in vascular endothelial cells and cytokine production from macrophages. By suppressing the initial inflammatory process associated with early atherosclerosis, increasing levels of adiponectin lower the risk for cardiovascular sequelae of metabolic dysfunction (Diez and Iglesias, 2003). The combination of improving IS while lowering inflammation has generated strong interest in metabolic treatments that increase adiponectin.

The relationship between drug-naïve schizophrenia and adiponectin is unclear. An initial study found lower serum adiponectin levels in normal-weight, first-episode, drug-naïve, people with schizophrenia (Song et al., 2013). A recent meta-analysis was unable to confirm the finding, though did show a trend level of significance (p = 0.09) for decreased adiponectin in drug-naïve individuals with schizophrenia (Bartoli et al., 2015). The result was notable for significant heterogeneity within the sample, which indicates that there is a subset of people with schizophrenia who have abnormally low baseline adiponectin levels and may be at greater risk for APD-induced metabolic side effects. The introduction of APDs has been consistently shown to lower adiponectin levels (Bai et al., 2009; Tanyanskiy et al., 2015). The drugs most associated with decreased adiponectin are also the drugs most associated with metabolic side effects, clozapine (p < 0.001) and olanzapine (p = 0.04; Bartoli et al., 2015). Furthermore, dopamine receptors in adipocytes appear to regulate adiponectin levels, which suggests a putative link between the D2R/D3R polymorphisms implicated in the pathogenesis of schizophrenia and adiponectin's role in mediating intrinsic metabolic disturbances in people with the illness. Likewise, D2R/D3R antagonism ubiquitous to APDs may also connect adiponectin to APD-induced metabolic dysfunction (Borcherding et al., 2011).

Given the above evidence that the machinery for monoamine synthesis and dopamine receptor signaling is present in insulin-sensitive peripheral organs, it suggests that: (1) APDs target the same monoamine receptors in the periphery as they do in the CNS, and (2) monoamines are not only involved in APD-induced IR, but in overall insulin homeostasis (Bailey, 2000). Increasing evidence points to a potentially important contribution by monoamines to APD-induced metabolic dysfunction through direct action of APDs on peripheral monoaminergic targets including those found in insulin-secreting pancreatic beta cells (Kalra et al., 2011). This may therefore be consistent with D2R/D3R agonist bromocriptine's efficacy in the treatment of T2D based on findings that it reduces plasma glucose, triglyceride, and free fatty acid levels, and significantly decreases hemoglobin A1C levels as compared to placebo (Holt et al., 2010; Valiquette, 2011). (2) Robust metabolic findings have also been seen in obese, non-diabetic people comparable at least by metabolic markers to people with schizophrenia (Kamath et al., 1997; Kok et al., 2006).

In sum, it is increasingly evident that effects on metabolism caused by APD treatment and/or those intrinsic to schizophrenia are due to convergent effects on signaling through monoamines or neuromodulators in both the CNS and periphery. Metabolically-relevant tissues in the CNS and periphery share many of the same receptor signaling systems including the dopamine, serotonin, and histamine systems. Moreover, changes in central brain metabolic regulation propagate to target organs in the periphery including the gastrointestinal system (GI), pancreas, muscle, liver, and adipose tissues and these tissues respond in a reciprocal manner. Because APDs act concurrently on multiple receptor systems in all these tissues (e.g., dopamine, serotonin, histamine), the effects of these drugs' disruptions on metabolic regulation and appetite are likely cumulative and lead to the metabolic imbalances observed clinically (Figure 1).

To date, there is little definitive evidence of genes associated with schizophrenia having a causative role in the development of metabolic sequelae associated with either APD-induced metabolic disturbances or with the illness' intrinsic metabolic risk. Nevertheless, two genes associated with schizophrenia, AKT1 and neuregulin 1 (NRG1), may also play roles in systemic metabolic regulation. This suggests that mutations or polymorphisms in these genes may have roles not only in neuronal function within the CNS associated with schizophrenia's neuropsychiatric symptoms, but that these disruptions of function may also affect more global metabolic functions in other pathways including within insulin signaling in organs such as, the pancreas and liver.

We suggest a model, based on the above evidence, in which the disease processes underlying schizophrenia also confer a significant intrinsic risk for development of metabolic disturbances including IR and T2D. Consequently, these processes not only affect regions of the CNS implicated in the neuropsychiatric symptoms classically associated with the disorder including cognition, executive function and sensory perception, but likely target metabolically-relevant areas as well. Changes in neurotransmission within CNS regions such as, the hypothalamus in schizophrenia may also feedback upon metabolic signaling in the periphery including the endocrine pancreas, liver, and adipose tissue (Figure 1). Moreover, because these central and peripheral targets likely rely on conserved signaling pathways and molecules (e.g., dopamine, AKT1, NRG1), disruption or changes in these pathways in one organ may also be found in others and consequently may explain the reciprocal connections between multiple systems. Furthermore, APDs may further alter metabolism through their actions at these same CNS and peripheral sites of action. This results in APD-induced exacerbation of the preexisting metabolic risks intrinsic to schizophrenia. Along with additional lifestyle and environmental factors, the combination of APD-induced and intrinsic risk factors leads to the serious metabolic dysfunctions described clinically (Karam et al., 2010; Ballon et al., 2014). Therefore, improving our understanding of both the processes responsible for metabolic regulation and schizophrenia may elucidate the common mechanisms between both and ultimately inform new drug development strategies.

ZF and JB: Conception and design, manuscript writing, editing and figure design. DA: Experimental work and conception and figure design. RS: Manuscript writing, editing, synthesis of previous literature and figure design.