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International Clinical Psychopharmacology:
doi: 10.1097/YIC.0b013e3283400cd3
Original Articles

Relationship between insulin resistance and C-reactive protein in a patient population treated with second generation antipsychotic medications

Kim, Sun H.a; Reaven, Geralda; Lindley, Stevenb,c

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Author Information

Departments of aMedicine

bPsychiatry, Stanford University School of Medicine, Stanford

cVeterans Affairs, Palo Alto Health Care System, Palo Alto, California, USA

Correspondence to Dr Sun H. Kim, MD, MS, Stanford University Medical Center, 300 Pasteur Drive, Room S025, Stanford, CA 94305-5103, USA Tel: +1 650 723 8284; fax: +1 650 725 7085; e-mail:

Received April 26, 2010

Accepted August 31, 2010

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C-reactive protein (CRP) is an inflammatory marker associated with obesity, insulin resistance, and cardiovascular disease. A recent study found CRP levels to be higher in individuals treated with certain antipsychotic medications such as olanzapine; however, it is not clear whether this is associated directly with drug intake or indirectly with drug-associated weight gain and insulin resistance. The objective of this study was to explore the potential predictors of CRP including insulin resistance, components of the metabolic syndrome, psychiatric diagnosis, and antipsychotic medication in patients treated with antipsychotics. Sixty-four outpatients without diabetes being treated with a single second generation antipsychotic medication had direct measurements of insulin resistance at the end of a 180-min infusion of glucose, insulin, and octreotide (insulin suppression test) as well as components of the metabolic syndrome. Insulin resistance was the strongest predictor of CRP (r=0.52, P<0.001). When adjusted for insulin resistance, there was no significant relationship between CRP and any of the components of the metabolic syndrome criteria, specific drug treatment or psychiatric diagnoses. In conclusion, insulin resistance is strongly associated with CRP levels and likely contributes to earlier associations between CRP and certain antipsychotic treatments.

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Treatment with second generation antipsychotic (SGA) medications is associated with the development of metabolic complications, such as type 2 diabetes (Sernyak et al., 2002; Lamberti et al., 2004; Smith et al., 2008). As SGA treatment is also associated with weight gain, it has been difficult to delineate the effects secondary to weight gain from direct drug effects. For example, olanzapine treatment may have both weight-dependent (Sowell et al., 2002; Sacher et al., 2008; Newcomer et al., 2009) and weight-independent (Newcomer et al., 2002; Henderson et al., 2005; Henderson et al., 2006; Kim et al., 2009) associations with insulin resistance. Olanzapine treatment has also been associated with the increase in circulating markers of inflammation, including C-reactive protein (CRP), in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (Meyer et al., 2009). Low-grade inflammation may underlie many disease processes including type 2 diabetes (Ndumele et al., 2006) and may provide a novel mechanism for SGA-associated metabolic complications. On the other hand, CRP can also increase with obesity-associated insulin resistance (McLaughlin et al., 2002; Abbasi et al., 2008) and may only be indirectly increased with SGA treatment.

To the best of our knowledge, we are not aware of any study that has evaluated the relationship between CRP and a direct measure of insulin resistance in a population of patients treated with SGAs. In CATIE, CRP was associated with meeting greater numbers of the metabolic syndrome criteria which includes five components associated with insulin resistance (Meyer et al., 2009). However, insulin resistance was not directly measured.

The purpose of this study, therefore, was to assess the relationship between CRP and insulin resistance as measured directly by the insulin suppression test in a population of patients treated with a single SGA. We also explored other variables associated with CRP such as components of the metabolic syndrome criteria, psychiatric diagnoses, and SGA treatment. We hypothesized that insulin resistance would be the major predictor of CRP.

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Participants included 64 outpatients (36 male, 28 female) who were taking a single SGA for at least 3 months. A subset of the study population was recently included in a study evaluating the relationship between the body mass index (BMI) and insulin resistance in patients on SGA (Kim et al., 2009). SGAs included olanzapine (n=19), aripiprazole (n=19), risperidone (n=16), ziprasidone (n=6), clozapine (n=3), and quetiapine (n=1). Patients had to be on a stable antipsychotic regimen for 3 months preceding the study. The majority of the patients were taking SGAs for either schizophrenia/schizoaffective disorder (n=30) or bipolar disorder (n=27). Other diagnoses included depression (one), anxiety (three), posttraumatic stress disorder (two), and obsessive-compulsive disorder (one). No one was treated with a first generation antipsychotic medication. Individuals were in good general health without diabetes (fasting glucose <7 mmol/l), anemia, or serious heart, liver or kidney disease.

The study was approved by the institutional review boards of Stanford University and the Palo Alto Veteran Administration Research and Development Committee. Participants were recruited through outpatient psychiatric clinics associated with Stanford University and Palo Alto Veteran Administration and through local providers and advertisements. Informed consent was obtained from all participants, and the study was conducted in accordance with the Declaration of Helsinki.

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Insulin resistance

Insulin resistance was estimated by a modification (Pei et al., 1994) of the original insulin suppression test (Shen et al., 1970) which has a high correlation (r=0.93) with the euglycemic clamp technique (Greenfield et al., 1981). After an overnight fast, an intravenous catheter was placed in each of the individual's arms. One arm was used for the administration of a 180-min infusion of octreotide (0.27 mcg/m2/min), insulin (32 mU/m2/min), and glucose (267 mg/m2/min); the other arm was used for collecting blood samples. Octreotide is used to inhibit endogenous insulin production. Blood was drawn at baseline and at 10-min intervals from 150 to 180 min of the infusion to determine the steady-state plasma glucose (SSPG) and insulin concentrations. Because steady-state insulin concentrations are similar between individuals, the SSPG concentration provides a direct measure of the ability of insulin to mediate disposal of an infused glucose load; therefore, the higher the SSPG concentration, the more insulin resistant will be the individual.

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Metabolic syndrome criteria

The criteria for metabolic syndrome were derived from the recommendations of the National Cholesterol Education Program's Adult Treatment Panel III report (Grundy et al., 2005). To qualify for the metabolic syndrome, individuals needed to have three or more of the following five criteria: (i) waist circumference, ≥102 cm in males and ≥88 cm in females; (ii) blood pressure ≥130/85 mmHg or on an antihypertensive medication; (iii) triglycerides ≥1.695 mmol/l; (iv) high-density lipoprotein cholesterol (HDL-C) concentration of less than 1.036 mmol/l in men and less than 1.295 mmol/l in women; and (v) glucose ≥5.6 mmol/l.

Glucose concentrations were measured by the oxidase method (Beckman Analyzer 2, Brea, California, USA). Lipids were measured by the core laboratory at Stanford University Medical Center. Highly sensitive CRP was measure by Atherotech (Birmingham, Alabama, USA) using latex-enhanced turbidimetric in-vitro immunoassay with a lower level of detection of 0.05 mg/l.

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Statistical analysis

All statistical analysis was performed using SPSS (version 16 for Windows; SPSS, Chicago, Illinois, USA). Descriptive data are presented as median and interquartile range unless otherwise specified. Potential predictors of CRP were evaluated using a univariate and multivariate analysis with SSPG in the model. The following variables were not normally distributed and log-transformed before analysis: triglyceride, HDL-C, SSPG, and CRP.

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Table 1 represents the demographic and metabolic characteristics of the study population. Two-thirds were obese (BMI ≥30 kg/m2). Half (52%) met criteria for metabolic syndrome with the majority meeting criteria for high waist circumference (81%) and low HDL-C concentration (72%).

Table 1
Table 1
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Univariate analyses were performed to evaluate the relationship between CRP and SSPG concentration and components of the metabolic syndrome criteria individually and by the number of criteria met (Table 2). The strongest univariate predictor of CRP was SSPG concentration followed by waist circumference. Triglyceride concentration and the number of metabolic syndrome criteria were weaker predictors of CRP. When SSPG concentration was included as a covariate, none of the other variables remained significantly associated with CRP (P≥0.10).

Table 2
Table 2
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We also evaluated the relationship between CRP and psychiatric diagnoses or specific drug treatment in an analysis of covariance adjusted for SSPG concentration. There was no significant difference in CRP levels between the individuals with or without schizophrenia (natural log of CRP, mean±SEM, 0.76±0.18 vs. 0.81±0.17, P=0.83) or with or without bipolar disorder (0.75±0.19 vs. 0.82±0.16, P=0.78). For drug treatment, we compared only individuals treated with olanzapine, risperidone, and aripiprazole as these drug groups had the most number of individuals. There was no significant difference in CRP levels among the three groups (olanzapine, 0.83±0.23; risperidone, 0.93±0.25; and aripiprazole 0.36±0.22, P=0.18).

As metabolic syndrome criteria are often used as a surrogate for insulin resistance, we evaluated the relationship between the number of criteria met and SSPG concentration in Fig. 1. Although there was a significant linear association between number of metabolic syndrome criteria and SSPG (r=0.55, P<0.001), there was also a tremendous degree of overlap in SSPG value in patients meeting from between 1 and 5 of the metabolic syndrome criteria. As the number of metabolic syndrome criteria increased, the SSPG concentration also increased; however, the magnitude of difference appeared greater between individuals who met no criteria and more than three compared with any difference among those individuals meeting three, four or five criteria.

Fig. 1
Fig. 1
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In a diverse population of psychiatric patients treated with a single SGA, we show that insulin resistance is the strongest predictor of CRP among the variables examined. In addition, insulin resistance may explain a significant amount of the variance in the relationship between CRP and other presumed predictors such as obesity and metabolic syndrome components in patients treated with SGAs.

In CATIE, patients treated with olanzapine for 3 months had the greatest increase in CRP compared with the three other SGAs and perphenazine (Meyer et al., 2009). Olanzapine treatment was also associated with the greatest increase in weight gain (Lieberman et al., 2005), but change in BMI accounted for less than 1% of the change in CRP. The investigators postulated that certain antipsychotics such as olanzapine may have direct effects on inflammation. However, insulin resistance was not measured, and olanzapine treatment can have both weight-dependent (Sowell et al., 2002; Sacher et al., 2008; Newcomer et al., 2009) and weight-independent (Newcomer et al., 2002; Henderson et al., 2005; Henderson et al., 2006; Kim et al., 2009) associations with insulin resistance. Thus, it is also possible that change in insulin resistance was also contributing to the difference in CRP concentration among the drug groups in CATIE.

Although no earlier study has evaluated the relationship between CRP and a direct measure of insulin resistance, there have been a few studies in patients treated with SGAs that have measured CRP concentrations and an indirect measure of insulin resistance, the homeostasis model assessment of insulin resistance (HOMA-IR). HOMA-IR is a calculation based on fasting glucose and insulin concentrations (Matthews et al., 1985). Although it correlates with direct measures of insulin resistance, HOMA-IR accounts for less than 40% of the variability in insulin resistance (Kim et al., 2004), and therefore is a less precise measure of insulin action than SSPG. Possibly a reflection of the increased variability of this measure, the two studies measuring CRP and HOMA-IR in patients switched to olanzapine are inconsistent. Thus, one study showed a significant increase in both the HOMA-IR and CRP concentrations after switching to olanzapine therapy in 60 patients with schizophrenia (Baptista et al., 2007a) whereas another in 37 patients with schizophrenia showed a decrease in HOMA-IR and no significant change in CRP levels (Baptista et al., 2007b). Another cross-sectional study reported no association between CRP concentrations and HOMA-IR in patients treated with clozapine, olanzapine, and first generation antipsychotics and in relatives of patients taking clozapine and olanzapine (Carrizo et al., 2008). The differences among these results and with ours may be secondary to limitations of HOMA-IR as a surrogate of insulin resistance and/or to differences among the study populations. For example, while Carrizo et al. found no association between CRP and HOMA-IR, CRP was positively associated with BMI and waist circumference–variables that normally increase with the measures of insulin resistance (Farin et al., 2006). In addition, the patients treated with first generation antipsychotics had the highest CRP concentration among all the drug and nondrug groups, but they were also the leanest and had the lowest prevalence of the metabolic syndrome. This is contrary to the positive association seen between CRP and BMI in their study and contrary to the positive association seen between CRP and metabolic syndrome in our study and others (Ridker et al., 2003; Meyer et al., 2009). Despite these differences, one consistent finding among all the studies is that the change in CRP concentration seem unrelated to the type of antipsychotic treatment.

Although insulin resistance was the strongest predictor of CRP in our study, our results also show that it explains only a quarter of the variance in CRP. This degree of association is similar to results recently reported in the general population when insulin resistance was measured using the euglycemic clamp (r2=0.23) (Bluher et al., 2005). That insulin resistance does not account for the majority of the variance in CRP should not be surprising given that CRP is a nonspecific marker of inflammation and also varies with infection and other inflammatory processes (Du Clos and Mold, 2004). CRP has also been associated with various psychiatric phenotypes (Fan et al., 2007; Akanji et al., 2009). However, as insulin resistance was not assessed, these psychiatric illness associations may be mediated by differences in insulin action rather than specific disease presentations. Therefore, caution should be taken in associating CRP with clinical variables when insulin resistance has not been evaluated.

On this note, metabolic syndrome criteria are often used as a surrogate of insulin resistance. However, as seen in Fig. 1, there can be great overlap in degree of insulin resistance between individuals who meet varying numbers of the criteria. This likely explains the weaker correlation between CRP and number of metabolic syndrome criteria (r=0.25) compared with SSPG (r=0.52). CATIE reported an almost identical relationship between number of metabolic syndrome criteria and CRP (r=0.27) in their population. Therefore, metabolic syndrome criteria have some limitations as a surrogate measure of insulin resistance.

A major limitation of this study is that the study population is relatively small; therefore the smaller magnitude associations may not be detected. In addition, as it was a cross-sectional study, only associations and not causations can be shown. However, this is the first study to report on the relationship between CRP and insulin resistance using a direct measure of insulin action in a population of diverse patients treated with SGAs. In doing so, we find that insulin resistance is the major predictor of CRP among the variables evaluated and may explain some of the variance in CRP concentration in individuals treated with antipsychotics with different metabolic risks.

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Funding support was provided by Eli Lilly (Indianapolis, IN) and NIH Research Grants RR-00070, K23 MH079114 (S.H.K.).

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CRP; insulin resistance; metabolic syndrome; obesity; olanzapine; second generation antipsychotics

© 2011 Lippincott Williams & Wilkins, Inc.


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