Metabolic Abnormalities Course

Chapter1: DIABETES
Diabetes mellitus is a disease with a history going back to ancient times, yet it is a growing scourge in the modern world. Most broadly, diabetes can be defined as disorders of nutrient metabolism that result in abnormalities in circulating glucose and, frequently, lipids. These abnormalities confer an increased risk for vascular diseases, infectious complications, and other morbidities. It is currently estimated that nearly 20 million Americans, 7%–8% of the population, have diabetes (Boyle et al. 2001). Although a number of distinct syndromes exist, diabetes is generally caused by impaired insulin secretion with or without abnormal sensitivity to insulin action. The vast majority of affected patients have either type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM), and although these diseases share several clinical features, their pathogenesis is markedly different (Table 1–1). Of the two types of diabetes, T2DM is eight to nine times more common and is closely related to obesity, a problem that is on the rise worldwide. Although T1DM was previously called juvenile-onset diabetes and T2DM was called adultonset diabetes, it is now clear that both diseases affect people from childhood to older age. Because the incidence of both types of diabetes has increased markedly since the 1980s—a trend that is predicted to continue (Boyle et al. 2001)—diabetes will be one of the major public health burdens for the foreseeable future. Large intervention studies initiated in the late 1970s have firmly established the role of chronic hyperglycemia in causing microvascular complications, including renal, retinal, and neurological disease (American Diabetes Association 2003a, 2003b). It is also clear that the disordered metabolism of diabetes contributes to macrovascular disease, with increased rates of cardiac, cerebral, and peripheral arterial disease in diabetic persons (American Diabetes Association 1998; Kannel and McGee 1979; Stamler et al. 1993). These long-term complications of diabetes account for the dramatically increased morbidity and mortality among diabetic patients, and thus prevention of end-organ disease is the principal goal of diabetic treatment. Diabetes accounts for a disproportionate share of medical costs in the United States because of the prevalence of the condition and the burden of its associated morbidities. Estimated direct costs in 2002 were approximately $100 billion, with expenses due to work losses, disability, and mortality approaching another $40 billion (Hogan et al. 2003). On the basis of 2002 data, 20% of healthcare expenditures were attributable to patients with diabetes (Hogan et al. 2003). Given the increasing rates of diabetes, and especially its occurrence in younger people, the overall impact of diabetes on the American healthcare system is truly daunting

Important definitions related to diabetes
Epidemiology
Pathogenesis
Clinical Features
Diagnosis
Complications
Treatment
Conclusion
References

Chapter 2 : THE METABOLIC SYNDROME
THE METABOLIC SYNDROME Robert M. McCarron, D.O. Craig R. Keenan, M.D. The metabolic syndrome is a compilation of established individual risk factors for coronary artery disease (CAD) with the potential to dramatically increase the incidence of heart disease and diabetes mellitus, and all cause mortality (Grundy et al. 2004a; Lakka et al. 2002). Hypertension, dyslipidemia, glucose dysregulation, and obesity are the core components of the metabolic syndrome and are closely associated with the one in five adult deaths from heart disease in the United States (American Heart Association 2006). Patients with psychiatric disorders have a heightened risk because they often receive suboptimal preventive medical care and are more likely to be overweight and to present with metabolic abnormalities (Druss and Rosenheck 1998; Druss et al. 2002). Defining the Metabolic Syndrome The assemblage of metabolic disturbances as a distinct entity was described over 80 years ago by Kylin (1923) when he found that hyperglycemia, gout, and hypertension were correlated with negative cardiovascular outcomes. In 1988, Reaven proposed the term “syndrome X” to identify the CAD risk factors of dyslipidemia, hyperglycemia, and hypertension. Currently, the metabolic syndrome—sometimes referred to as the “deadly quartet,” “dysmetabolic syndrome,” and “insulin resistance syndrome”—has three commonly used definitions developed by the World Health Organization (WHO), the American Association of Clinical Endocrinologists (AACE) and the National Cholesterol Education Program–Adult Treatment Panel III (NCEP-ATP III), respectively (Table 2–1). Although there are several distinctions, these descriptions share many common features while stressing the importance of CAD risk assessment. TABLE 2–1. Common definitions of the metabolic syndrome Component Abnormality NCEP-ATP III Three or more of the following: Abdominal obesity (waist circumference) Men >40 inches (>102 cm) Women >35 inches (>88 cm) Triglyceridesa ≥150 mg/dL (1.695 mmol/L) HDL cholesterol Men <40 mg/dL (1.036 mmol/L) Women <50 mg/dL (1.295 mmol/L) Blood pressure ≥130/≥85 mm Hg Fasting glucosea,b ≥100 mg/dL (5.6 mmol/L) World Health Organization Insulin resistance (indicated by one of the following) Type 2 diabetes Impaired fasting glucose Impaired glucose tolerance For those with normal fasting glucose, glucose uptake below the lowest quartile for background population under investigation under hyperglycemic, euglycemic conditions TABLE 2–1. Common definitions of the metabolic syndrome (continued) Component Abnormality World Health Organization (continued) Plus two of the following: Blood pressure Antihypertensive medication and/or blood pressure ≥140 mm Hg/≥90 mm Hg Plasma triglycerides ≥150 mg/dL (1.695 mmol/L) HDL cholesterol Men <35 mg/dL (0.897 mmol/L) Women 30 kg/m2 and/or waist-to-hip ratio > 0.9 inches in men and > 0.85 inches in women Renal function/Kidney disease Urinary albumin excretion rate ≥ 20 µg/min or albumin:creatinine ratio ≥ 30 mg/g American Association of Clinical Endocrinologists Diagnosis depends on clinical judgment based on risk factors. Obesity Body mass index ≥ 25 kg/m2 Plasma triglycerides ≥150 mg/dL (1.695 mmol/L) HDL cholesterol Men <40 mg/dL (1.036 mmol/L) Women 140 mg/dL (7.8 mmol/L) Other risk factors Family history of type 2 diabetes, hypertension, cardiovascular disease Polycystic ovary syndrome Sedentary lifestyle Advancing age Ethnic group with high risk for type 2 diabetes or cardiovascular disease Note. HDL=high-density lipoprotein; NCEP-ATP III=National Cholesterol Education Program–Adult Treatment Panel III. aNo caloric intake for 8 hours or more. bSuggested change by the American Diabetes Association from ≥110 mg/dL to ≥100 mg/dL. Source. Adapted from Grundy et al. 2004a. In 1998, the WHO developed the first definition of the metabolic syndrome and identified CAD as the primary clinical outcome (Alberti and Zimmet 1998). Under this definition, insulin resistance (or possible manifestations of insulin resistance) is a required component for the diagnosis. When insulin resistance is present, the diagnosis of the metabolic syndrome is established if two or more of the following are present: 1) elevated blood pressure and/or use of antihypertensive medications; 2) elevated plasma triglycerides; 3) decreased high-density lipoprotein (HDL) cholesterol; 4) increased body mass index (BMI) and/or increased waist-to-hip ratio; or 5) elevated urinary albumin excretion rate or albumin:creatinine ratio. It is often challenging to apply the WHO definition in clinical practice because the reliable determination of insulin resistance has significant time and cost limitations. Additionally, the measurement of insulin concentration and a corresponding abnormal range have not been sufficiently standardized. The AACE also has a strong focus on insulin resistance; they use the term insulin resistance syndrome to describe a more comprehensive group of risk factors. Under the AACE definition, diagnosis of the metabolic syndrome depends on clinical judgment, because there is no minimum number of factors that result in having the syndrome (Einhorn et al. 2003). The NCEP-ATP III definition of the metabolic syndrome is clearly defined, clinically useful, and often utilized to help assess risk of CAD. For a person to be diagnosed on the basis of the NCEP-ATP III guidelines, three of the following five abnormalities must exist: abdominal obesity, hypertension, decreased HDL cholesterol, hyperglycemia, or hypertriglyceridemia (National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults 2002). This classification does not specify if patients treated with antihypertensive agents or diabetic medications still meet the criteria for hypertension and hyperglycemia, respectively. A waist circumference of more than 40 inches (>102 cm) in men and more than 35 inches (>88 cm) in females is used to determine central obesity, which is a marker for more atherogenic intraabdominal or visceral obesity. Conversely, the WHO definition uses BMI to estimate intraabdominal obesity; this measure does not reliably reflect visceral adipose tissue in various populations, including most Asians, who have a lower threshold for a diagnosis of obesity (BMI>25) (World Health Organization 2004). This distinction is important because studies have demonstrated a higher concentration of adipose-related, deleterious free fatty acids (FFAs) en route to the liver by way of the splanchnic circulation in patients with demonstrated increased visceral fat relative to those with higher subcutaneous fat (Aubert et al. 2003; Jensen et al. 1989). PREVALENCE The prevalence of adult obesity in the United States has increased since the early 1990s from 22.9% in 1991 to 30.5% in 2004, with more than

Chapter 3 : SEVERE MENTAL ILLNESS AND OBESITY
Severe mental illness and obesity are each important public health problems that overlap to a significant degree, especially in clinical populations (McElroy et al. 2006a). The degree, nature, and causes of this overlap, however, are not well understood. Indeed, no major epidemiological study has yet evaluated the co-occurrence of obesity, as assessed using the definitions of the National Institutes of Health (NIH) (National Institutes of Health 1998), with the full range of psychotic and mood disorders, as defined by DSM-IV-TR (American Psychiatric Association 2000) or other widely accepted operational diagnostic criteria. Also, the treatment of patients with both severe mental illness and obesity has received little empirical study. Therefore, to elucidate present knowledge about the relationship between obesity and severe mental illness, we first present an overview of obesity and the obesity-related conditions: overweight, abdominal obesity, and the metabolic syndrome. Next, we review the relationship between severe mental disorders, especially psychotic and mood disorders, and obesity. We then review psychiatric, behavioral, medical, and surgical treatments available for the obese patient with a severe mental disorder.

Chapter4: SEVERE MENTAL ILLNESS AND DIABETES MELLITUS
in 1879, Sir Henry Maudsley observed that “diabetes is a disease which often shows itself in families in which insanity prevails” (Maudsley 1879). That patients with se vere mental illness, schizophrenia, bipolar disorder, and major depressive disorder are at increased risk for diabetes has been observed for nearly a century and well prior to the modern era of pharmacotherapy of mental illness heralded by John Cade’s dis covery, in 1949, of lithium’s therapeutic effects (Cade 1949). This association between mental illness and diabetes has been the subject of renewed scientific attention because of concerns that treatment with atypical antipsy chotic agents might increase the risk of type 2 diabetes mellitus (T2DM; Keck et al. 2003). However, careful scrutiny of the literature also reveals that similar concerns were raised after the introduction of lithium and typical antipsychotics (neuroleptics; Zimmerman et al. 2003). Thus, in attempting to understand the potential risks asso ciated with treatment of these psychiatric disorders, it is important to discern the risks for diabetes posed by the illnesses themselves. In this chapter, we review the pre–mod ern era (prior to 1949) literature regarding the risk of diabetes in patients with severe mental illness, subsequent studies in the modern era (1949 and thereafter), and pre clinical and clinical data regarding possible mechanisms underlying this risk.

Chapter5: CARDIOVASCULAR DISEASE
Cardiovascular disease (CVD) is the most pressing healthcare problem and lead ing cause of death worldwide (Bonow et al. 2002). The World Health Organiza tion (WHO; 2005) estimates approximately 16.7 million people around the globe die annually of CVD, particularly from heart attacks and strokes. With 80% of the burden in low- and middle-income countries, the problem has spread beyond Westernized societies. Despite the advent of coronary care units, availability of reperfusion technologies, increased public access to defibrillators, and medications such as aspirin and beta blockers, CVD accounted for 38% of all deaths in the United States in 2002 (American Heart Association 2005). Persons with chronic and severe mental illness are at greater risk for cardiovas cular morbidity and mortality compared with the general population. Patients with schizophrenia have a significantly increased burden of CVD versus the gen eral population, and cardiovascular mortality contributes to the excess mortality associated with schizophrenia. A variety of studies of different methods and de signs have confirmed this association. In a retrospective cohort study in which 3,022 individuals with schizophrenia were identified from a Canadian health database, researchers found an increased incidence of ventricular arrhythmia, heart failure, stroke, and cardiovascular mortality compared with age- and sex matched, randomly selected, general population control subjects (Curkendall et al. 2004). Recent data from the Clinical Antipsychotic Trials of Intervention Effective ness (CATIE) study confirm the findings of greater risk for CVD in severely mentally ill populations. Goff et al. (2005), using the Framingham CHD risk function, calculated the 10-year risk for coronary heart disease (CHD) for 689 subjects with schizophrenia at baseline. Compared with age-, race- and gender matched control subjects, both male (9.4% vs. 7.0%) and female (6.3% vs. 4.2%) subjects had an elevated 10-year risk for CHD. Schizophrenia patients had higher rates of smoking, diabetes, and hypertension, and lower high-density lipo protein (HDL) cholesterol values, compared with the control group. McEvoy et al. (2005) used baseline data from the CATIE study and found that males were 138% more likely to have the metabolic syndrome and females were 251% more likely, compared with a national comparison group, after controlling for age and race. Persons with the metabolic syndrome are at increased risk for CVD. These data are consistent with the excess cardiovascular mortality reported in previous studies of patients with schizophrenia (Allebeck 1989; Brown et al. 2000; Mor tensen and Juel 1990; Osby et al. 2000) and in Brown’s 1997 meta-analysis of mor tality studies published from 1952 to 1996. Patients with mood disorders also have increased mortality from CVD. In a systematic review of the mortality of depression, Wulsin et al. (1999) reviewed 57 studies published from 1966 through 1998. They found that although the stud ies linking depression to early death were poorly controlled, 29 (51%) of the 57 studies reviewed suggested depression increased the risk of death by CVD. Angst et al. (2002) prospectively followed hospitalized patients with bipolar or unipolar disorder for 22 years or more and found an elevated standardized mortality ratio (SMR) for CVD. In the subgroup analysis of unipolar and bipolar patients, uni polar patients had an SMR of 1.36 (not significant) for CVD, and bipolar pa tients had an SMR of 1.84 (P<0.05). In a population study conducted in Sweden (Osby et al. 2001), all patients with a hospital diagnosis of bipolar (n=15,386) or unipolar (n=39,182) disorder from 1973 to 1995 were identified and linked with the national cause-of-death register to determine the date and cause of death. In this sample, male or female patients with either bipolar or unipolar disorder had significantly increased SMRs for CVD compared with the general population. Why do the mentally ill have an increased risk of mortality from CVD? First, mental illness is associated with a number of changes in an individual’s health that may influence the development and course of CVD. Second, mental illness is associated with physiological changes that negatively affect the cardiovascular system. Finally, there may be an underlying factor such as stress that leads to the development of both mental illness and CVD (Figure 5–1). This chapter addresses 1) potential relationships between mental illness and CVD, with a particular emphasis on traditional cardiac risk factor prevalence rates among the mentally ill, and 2) practical management of cardiac risk factors to assist psychiatrists in quantifying and reducing their patients’ cardiac risk.

Chapter6: ANTIPSYCHOTIC-ASSOCIATED WEIGHT GAIN
Antipsychotics, both first and second generation, are broad-spectrum neurother apeutic agents capable of attenuating myriad psychopathological symptoms. As a class of agents, the second-generation antipsychotics are promoted as offering sev eral therapeutic advantages when compared with the older first-generation agents (e.g., enhanced efficacy for neurocognitive deficits, reduced propensity for neuro logical adverse events). Nevertheless, significant weight gain associated with many of the available second-generation drugs detracts from their therapeutic potential and patient acceptance. In this chapter, we aim to provide the practicing clinician with a synthesis of the extant literature reporting on the association between antipsychotic usage and weight gain. The chapter is organized into three areas of focus: 1) the liability and correlates of antipsychotic-associated weight gain (AAWG); 2) the putative mech anisms of AAWG; 3) and the strategies preliminarily evaluated as management ap proaches for AAWG. We conducted a Medline search of all English-language articles from 1966 to 2005, using the following keywords: overweight, obesity, body mass index (BMI), weight gain, schizophrenia, psychosis, psychotic disorders, bipolar disorder, majo depressive disorder, conventional antipsychotics, atypical antipsychotics, cloza pine, olanzapine, risperidone, quetiapine, ziprasidone, aripiprazole, genetics, pep tides, leptin, weight loss, bariatric, topiramate, sibutramine, orlistat, behavioral therapy, Weight Watchers, and antidepressants. The search was supplemented with a manual review of summary articles and references germane to this topic. Through out the chapter, the use of the word significant denotes statistical significance of P<0.05

Chapter7: GLUCOSE METABOLISM
A range of evidence suggests that treatment with some antipsychotic medications is associated with an increased risk for insulin resistance, hyperglycemia, and dys lipidemia compared with no treatment or treatment with alternative antipsychot ics (Newcomer 2005). Evidence for drug effects on these risk factors for type 2 diabetes mellitus (T2DM), along with the results of population-based analyses of diabetes incidence during antipsychotic treatment, suggests that these same anti psychotics do increase risk for T2DM (Newcomer 2005). Interpretation of the lit erature has been complicated by a small number of reports suggesting that patients with mental disorders such as schizophrenia have an increased prevalence of ab normalities in weight regulation and glucose metabolism (e.g., insulin resistance) prior to initiation of antipsychotic therapy (Kasanin 1926). However, these studies have produced inconsistent results that are difficult to interpret (Reynolds 2006; Ryan et al. 2003; Thakore 2005; Zhang et al. 2004). For example, one study found evidence of impaired fasting glucose in acutely hospitalized, unmedicated patients with elevated plasma cortisol levels, in whom hypercortisolemia may con tribute to an increase in plasma glucose (Ryan et al. 2003). Hypercortisolemia, however, is not typically observed in chronically ill outpatients with schizophrenia or in antipsychotic-treated patients (Newcomer et al. 2002), so this study may overes timate the degree of hyperglycemia that could persist past an acute episode, hospi talization, and/or the agitated state. Further complicating this literature, early studies in this area (see Haupt and Newcomer 2001a, 2002) did not control for age, body weight, adiposity, ethnicity, and/or diet and activity levels, suggesting that differ ences in key factors such as diet and activity level between patients and control subjects may contribute to some or all of the observed abnormalities. In contrast to the limited conclusions that can be drawn from the small literature on unmed icated patients, there is a large literature concerning medication effects that indi cate a consistent effect of certain medications on risk for abnormalities in glucose metabolism. This chapter examines the evidence for an association between dysregulation of glucose and lipid metabolism and the related risk of T2DM during treatment with any of the six second-generation antipsychotics currently available in the United States: clozapine, risperidone, olanzapine, quetiapine, ziprasidone, and aripiprazole. Literature references were identified primarily via Medline searches. In addition to the Medline searches, abstracts presented at selected scientific meetings were searched. Finally, published reports of key pivotal studies examin ing the safety and efficacy of the different second-generation antipsychotics in patients with schizophrenia were reviewed for glucose and lipid data. The reports identified in this review can be broadly divided into three categories (Table 7–1): 1) case reports, chart reviews, U.S. Food and Drug Administration (FDA) Med Watch–based reports, and other uncontrolled observational studies; 2) large, con trolled, observational database analyses using prescription, administrative, or (less commonly) population-based databases; and 3) controlled experimental studies, including randomized clinical trials, although not all categories of study are avail able for each antipsychotic agent. These three categories of reports provide differ ent levels of evidence to assess the impact of antipsychotic agents on the different metabolic parameters. Case reports, chart reviews, and open observational studies all provide un controlled, largely anecdotal evidence and are generally useful for hypothesis generation only. Relevant controlled observational database analyses, a few using population-based data, can provide higher or lower levels of evidence depending on the methodology and the study endpoints used. Finally, controlled experimen tal studies, including prospective, randomized, controlled clinical trials, are de signed to address specific questions and can be useful for “hypothesis testing.”

Chapter8: SERUM LIPIDS
Patients with severe mental illnesses, such as bipolar disorder and schizophrenia, are a medically vulnerable population at high risk for cardiovascular mortality, with standardized mortality ratios from cardiovascular disease two times greater than the general population (Osby et al. 2000, 2001). Much of psychiatric care is focused on suicide prevention, yet cardiovascular disease remains the single largest cause of death among males and females with schizophrenia. Given this sobering data, it is imperative that those who care for the severely mentally ill have a working knowledge of the risks associated with cardiovascular disease, and the patterns of risk factors seen in this patient population. Recogni tion and treatment of diabetes mellitus has been covered elsewhere in this volume (see Chapter 1, “Diabetes: An Overview,” and Chapter 4, “Severe Mental Illness and Diabetes Mellitus,” this volume), but the importance of diabetes relates not only to the adverse effects of hyperglycemia but also to its impact on cardiovascu lar risk. Diabetes mellitus is now a condition considered equivalent in future risk for major cardiovascular events (e.g., myocardial infarction [MI], sudden death) to having established coronary heart disease (CHD) (Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults 2001). This view of diabetes-related CHD risk is based on data that show patients with estab lished diabetes have the same future MI incidence as patients without diabetes who have had an MI (Haffner et al. 1998). For nondiabetic persons, the focus remains on modifying the traditional risk factors of hypertension, hyperlipidemia, and smoking. Baseline data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study pro vide the most timely and complete picture of the risk patterns in chronic schizo phrenia patients residing in the United States (Goff et al. 2005). Among the 1,460 schizophrenia patients assessed upon study entry, 10-year CHD risk, calcu lated by using Framingham scores, was significantly elevated in males (9.4% vs. 7.0%) and females (6.3% vs. 4.2%), with schizophrenia patients compared with age-, gender-, and race/ethnicity-matched control subjects from a general popula tion database (P=0.0001). In particular, schizophrenia patients had significantly higher rates of smoking (68% vs. 35%), diabetes (13% vs. 3%), and hypertension (27% vs. 17%) and lower high-density lipoprotein (HDL) cholesterol levels (43.7 vs. 49.3 mg/dL) compared with control subjects (P<0.001). Moreover, CATIE subjects also had greater prevalence of central adiposity and elevated serum triglycerides, both of which are components of the metabolic syndrome and are associated with insulin resistance and future diabetes risk (McEvoy et al. 2005). The importance of monitoring serum triglyceride values during antipsy chotic treatment will become readily apparent, because this is the lipid parameter most greatly affected by offending medications. Induction of hyperlipidemia during antipsychotic therapy thus represents a serious condition not only because of its inherent impact on cardiovascular risk but also because it is occurring in a group that possesses considerable risk (Saari et al. 2004). What has become evident in recent years is that the atypical antipsy chotics have a decreased liability for neurological side effects, but certain agents in this class have a marked propensity for adverse metabolic outcomes, especially hyperlipidemia (Meyer and Koro 2004). Given the widespread use of atypical antipsychotics for disorders beyond schizophrenia and bipolar disorder, this review is intended to guide the clinician in choice of medications and appropriate monitoring strategies for hyperlipidemia, presenting the best available data. This discussion is bolstered by the recent publication of the double-blind, controlled data from Phases I and II of the CATIE Schizophrenia Trial (Lieberman et al. 2005; Stroup et al. 2006). The large population under study in the CATIE study is one of the best sources of prospective information for certain compounds, espe cially quetiapine, for which prospective data were sorely lacking. There is increased interest in improving medical outcomes for severely men tally ill patients (Marder et al. 2004; Meyer et al. 2006), so minimization of iatro genically induced lipid problems and appropriate monitoring of those at risk are increasingly becoming the standards of care for this patient population. Recogni tion of which antipsychotics impose the greatest risk for hyperlipidemia and an understanding of the common dyslipidemia patterns seen during use of these anti psychotics are necessities for providing high-quality care to antipsychotic-treated patients.

Chapter9: METABOLIC RISK ASSESSMENT, MONITORING, AND INTERVENTIONS
Clinicians are well informed of the metabolic risks of the atypical antipsychotics (American Diabetes Association et al. 2004; Expert Group 2004; Lambert et al. 2004; Marder et al. 2004). A recent survey indicated psychiatrists commonly iden tify the metabolic risks of weight gain and premature diabetes associated with atypical antipsychotics and regularly monitor patients’ weight. However, psychia trists differ as to whether they are responsible for monitoring the general medical conditions of the mentally ill and rarely report monitoring waist circumference, lipid profiles, or blood pressures of their patients (Newcomer et al. 2004). Al though psychiatrists perceive their patients to be at significant risk for metabolic problems versus the general population, it is unclear how they come to this con clusion with individual patients. That is, which particular metabolic problems or parameters are used by psychiatrists in determining a patient’s metabolic risk? Do psychiatrists consider a patient’s lipid profile, blood pressure, or family history as part of this calculation? What metabolic parameters, data, or history do they have or gather prior to starting pharmacotherapy? In this chapter we propose an assessment tool to evaluate patients’ metabolic risk. In addition to the fact that many psychiatric medications are associated with metabolic problems, there are a number of reasons why we propose a thorough metabolic risk assessment of each patient: • Cardiovascular disease (CVD) is the primary cause of death for both men and women in the United States and in most countries worldwide and correlates with other metabolic problems such as dyslipidemia, high blood pressure, and diabetes (Bonow et al. 2002). • CVD is a leading cause of death in persons with mental disorders. Individuals with mental illness possess a substantial burden of metabolic morbidity and die at an earlier age from these conditions versus the general population (Kil bourne et al. 2004). • There may be a correlation between the mechanisms leading to CVD and other metabolic problems and the mechanisms leading to mental illness, such as dysregulation of cortisol, coagulation, and inflammatory factors. • Lifestyle changes in diet, exercise, and tobacco use improve mental health and well-being. One of the best ways to monitor the success of a patient’s lifestyle changes is to monitor cardiovascular and diabetic risk markers. • Despite the evidence that psychiatric patients are at high risk for metabolic problems and poor health outcomes secondary to complications from diabetes and CVD, this is not true for all patients. Certainly we do not want to deprive patients of efficacious agents out of a blanket fear of metabolic complications, especially when a number of patients do not carry this risk. Predicting which patients will have medication-induced metabolic side effects is difficult. The majority of information in this area is on the atypical antipsychotics, although the studies are retrospective and involve secondary outcomes. Studies of olanzapine identify younger age, lower body mass index (BMI), positive clinical response, early rapid weight gain, and appetite increases to predict a final weight gain of 7% or more of premedication weight (Basson et al. 2001; Jones et al. 2001; Kinon et al. 2001). Similar factors predict weight gain associated with risperidone, clozapine, haloperidol, and other antipsychotics (Basson et al. 2001; Briffa and Meehan 1998; Hummer et al. 1995; Lamberti et al. 1992; Lane et al. 2003; Lead better et al. 1992; Meltzer et al. 2003; Umbricht et al. 1994; Wetterling and Mussigbrodt 1999). However, none of these studies controlled for diet, tobacco use, family history, or other baseline measures of metabolic status such as fasting glucose, waist circumference, or lipid profile. It is not known what predicts clini cally significant weight gain (≥7% baseline weight) or metabolic disturbances in ziprasidone- or aripiprazole-treated patients, although from studies and clinical use this appears to be an infrequent problem (Centorrino et al. 2005; Harvey and Bowie 2005). In addition, little is known about predictors of metabolic distur bances or weight gain with medications outside of the atypical antipsychotic class, such as valproic acid, lithium, and mirtazapine (Fawcett and Barkin 1998; Fisfalen and Hsiung 2003; Isojaryi et al. 1996; Livingstone and Rampes 2005). Given the difficulty of predicting metabolic side effects and the propensity of patients with mental disorders to have metabolic problems, we propose a thor ough cardiovascular and diabetic risk assessment prior to pharmacotherapy in order to better inform clinicians, patients, and their families of the particular car diovascular or diabetic risk they may face

About Course

Course Content

Important definitions related to diabetes

Epidemiology

Pathogenesis

Clinical Features

Diagnosis

Complications

Treatment

Conclusion

References

Defining the Metabolic Syndrome

The Metabolic Tetrad

Pathogenesis

Management of the Metabolic Syndrome

Future Considerations

References

Overview of Obesity

Obesity and Psychotic Disorders

Obesity and Mood Disorders

Treatment of the Severely Mentally Ill Patient With Obesity

Conclusion

References

Prevalence of Diabetes in Severe Mental Illness STUDIES BEFORE 1949

Potential Mechanisms Linking Severe Mental Illness and Type 2 Diabete

NEUROBIOLOGICAL FACTORS AFFECTING GLUCOSE REGULATION

References

Changes in Health

Physiological Impact of Mental Illness on Cardiovascular Disease

Antipsychotic-Associated Weight Gain: Liability and Correlates

Medication-Associated Weight Gain: Mechanisms

Prevention and Treatment of Medication-Associated Weight Gain

Conclusion

References

Level 1 Evidence: Case Reports and Other Uncontrolled Observational Studies

Level 2 Evidence: Observational Database Analyses

Level 3 Evidence: Controlled Experimental Studies, Including Randomized Clinical Trials

Monitoring and Treatment Considerations

Conclusion

References

Hyperlipidemia and Typical Antipsychotics

Hyperlipidemia and Atypical Antipsychotics

Patient Variables and Possible Mechanisms for Antipsychotic-Related Hyperlipidemia

Monitoring Recommendations for Hyperlipidemia During Antipsychotic Therapy

References

Initial Assessment

Selection of Treatment

Management of Metabolic Conditions

References

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