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Low non-oxidative glucose metabolism and violent offending: An 8-year prospective follow-up study
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Psychiatry Research 168 (2009) 26 – 31 www.elsevier.com/locate/psychres
Low non-oxidative glucose metabolism and violent offending: An 8-year prospective follow-up study Matti Virkkunen a,⁎, Aila Rissanen a , Anja Franssila-Kallunki a , Jari Tiihonen b,c a
Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland b Department of Forensic Psychiatry, University of Kuopio, Kuopio, Finland c Department of Clinical Physiology, Kuopio University Hospital, Kuopio; Finland
Received 11 October 2006; received in revised form 30 July 2007; accepted 22 March 2008
Abstract Violent offenders have abnormalities in their glucose metabolism as indicated by decreased glucose uptake in their prefrontal cortex and a low blood glucose nadir in the glucose tolerance test. We tested the hypothesis that low non-oxidative glucose metabolism (NOG) predicts forthcoming violent offending among antisocial males. Glucose metabolism was measured using the insulin clamp method among 49 impulsive, violent, antisocial offenders during a forensic psychiatric examination. Those offenders who committed at least one new violent crime during the 8-year follow-up had a mean NOG of 1.4 standard deviations lower than non-recidivistic offenders. In logistic regression analysis, NOG alone explained 27% of the variation in the recidivistic offending. Low non-oxidative metabolism may be a crucial component in the pathophysiology of habitually violent behavior among subjects with antisocial personality disorder. This might suggest that substances increasing glycogen formation and decreasing the risk of hypoglycemia might be potential treatments for impulsive violent behavior. © 2008 Elsevier Ireland Ltd. All rights reserved. Keywords: Aggression; Alcohol abuse; Forensic psychiatry; Metabolism; Personality disorders; Violence
1. Introduction A large proportion of violent offenses in Western countries are attributable to antisocial personality disorder (APD), which is associated with early onset alcoholism. In Finland, for example, offenders with APD commit up to 60–80% of the most severe recidivist violent offenses (Tiihonen and Hakola, 1994; Eronen
⁎ Corresponding author. Department of Psychiatry, Helsinki University Central Hospital, PO Box 320, FI-00029 HUCH, Helsinki, Finland. Tel.: +358 9 471 81 260; fax: +358 9 471 81 313. E-mail address: [email protected] (M. Virkkunen).
et al., 1996). Risk assessment of violent offending among this population has been considered to be problematic and of little practical value (Dolan and Doyle, 2000). Variables in criminal history, or actual measures of criminological variables, have been the best predictors, whereas clinical variables have provided the smallest effect sizes (Bonta et al., 1998; Gray et al., 2004). The only biological factors that have predicted violent offending in prospective follow-up studies have been low cerebrospinal fluid 5-hydroxyindoleacetic acid (CSF 5-HIAA) and low CSF 3-methoxy-4-hydroxyphenylglycol (MHPG), which are metabolites of the neurotransmitters serotonin and norepinephrine, respectively (Virkkunen et al., 1989, 1996).
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M. Virkkunen et al. / Psychiatry Research 168 (2009) 26–31
In a recent study using the insulin clamp/calorimetry method, we found that habitually violent male offenders with APD have very low non-oxidative glucose (NOG) metabolism (i.e., glycogen formation) when compared with normal male controls that were matched for both age and body mass index (BMI; Virkkunen et al., 2007). The aim of this prospective follow-up study was to test the hypothesis that low NOG metabolism is a trait that predicts recidivistic violent offending among habitually violent antisocial males. 2. Methods and materials 2.1. Protocol Informed consent was obtained from all participants after the procedure had been fully explained, and consideration for the appropriate protection of human rights was observed throughout the study. The study was approved by the Ethical Committee of the Helsinki University Central Hospital, Finland. Incarcerated subjects had the option of contacting a Finnish prison representative during the study. 2.2. Diagnostic and psychosocial assessment All subjects, including healthy subjects, were assessed with the Structured Clinical Interview for DSM-III-R (SCID, axes I and II; American Psychiatric Association, 1987). 2.3. Subjects This study included 69 habitually violent offenders who were admitted to a forensic psychiatric examination on a closed ward, and who also fulfilled the DSM-III-R criteria for APD and alcohol dependence (for additional details on co-morbidity, see Virkkunen et al., 2007). A diagnosis of psychosis was an exclusion criterion. Two subjects were later omitted because of their poor performance on an intelligence test (WAIS IQ 60–70). The subjects represented typical violent alcoholic offenders who had recently committed severe violent crimes (homicide, attempted homicide, aggravated assault, assault) and who had, on average, been incarcerated in prison for 3–6 months before examination. In the forensic mental examination, all the crimes were considered to be impulsive (not premeditated), and the offenses were committed under the influence of alcohol or illicit drugs in all 67 cases. This group did not have access to alcohol at least 3 months prior to attending the forensic mental examination and subsequent insulin clamp calorimetry at the Department of Psychiatry, Helsinki University Hospital. During the last week before
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insulin clamp and calorimetry measures, the prisoners were found to be completely drug- and medication-free on the day of arrival to the Forensic Psychiatric Department, as confirmed by urinalysis. During their week at the unit, the prisoners were allowed to move freely throughout the ward. All subjects ate the same food during the 24-hour period preceding the measurements. The initial insulin clamp with indirect calorimetry and other biological measurements were made after the first wash-out week, without medication or intensive physical training. However, none of the study subjects had used antidepressants or antipsychotics within the last 2 weeks prior to the insulin clamp measurement. During the pre-hospital period in prison, inmates had an opportunity to engage in physical training if they wished. All 67 subjects were followed for up to 8 years by using the Finnish central prison register to observe the duration of incarceration, and by using the national crime register database to identify and observe recidivist offenses after release from prison. Five subjects died during the follow-up period, 13 subjects remained incarcerated (and were not included in the analysis), and 49 subjects were released until the end of the follow-up period (December 2003). The endpoints for the follow-up were either the end of the follow-up period or a new incarceration in prison as a result of a recidive offence. All subjects who were eligible for the study participated in the insulin clamp and calorimetry measurements, whereas only 6 (35%) of the 17 recidivistic offenders and 11 (34%) of the 32 non-recidivistic offenders (Χ2 =0.00, P = 0.95) were willing to participate in the lumbar puncture (i.e., 5-HIAA measurements). Thirty-three (83%) of the 40 healthy subjects agreed to lumbar puncture. The results were contrasted with data obtained from healthy subjects (n=40), who were matched for both age and BMI (see Virkkunen et al., 2007), with the same procedure, and during the same period. The volunteers (students, nurses, policemen, firemen, postmen) were recruited through advertisements. In the advertisement, inclusion criteria were male gender, an age of more than 18 years and normal weight. No formal upper limits for age and weight were set in the advertisement. After the mean age and weight of the offenders started to emerge during the study, the healthy subjects were selected from the pool of volunteers to match the characteristics of the offenders. The advertisement further required that the volunteers had to be free of current or past drinking problems and mental disorders. 2.4. Euglycemic insulin clamp A 3-hour euglycemic, hyperinsulinemic clamp combined with indirect calorimetry was performed on all participants to estimate glucose oxidation (GOX), non-
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oxidative glucose metabolism (NOG) and lipid oxidation (LOX) while they remained in a recumbent position, beginning at 7:30 AM after a 12-hour overnight fast. These values were calculated on the basis of fat-free mass. Body fat and fat-free mass were assessed by bioelectrical impedance. Subjects received no medications prior to the study. After basal samples had been obtained, a primed continuous infusion of short-acting human insulin (Actrapid Human, Novo Nordisk, Copenhagen, Denmark) was administered at a rate of 45 mU/m2/min (ca. 340 pmol/m2/min) for 3 h. Plasma glucose concentration was determined at 5-min intervals, and 20% glucose was infused to maintain a constant plasma glucose concentration (see Franssila-Kallunki et al., 1992). Indirect calorimetry was analyzed in the basal state and after 150–180 min (Ferrannini, 1988) with a computerized, open-circuit system (Deltatrac, Datex, Helsinki, Finland) to measure gas exchange rates (i.e., CO2 production and O2 consumption; Meriläinen, 1987). The monitor had a precision of 2.6% for oxygen consumption and 1% for carbon dioxide production (Franssila-Kallunki, 1994). The amount of glucose that is required to maintain euglycemia is equivalent to the whole-body disposal of glucose (M-value), provided that there is no entry of glucose from the liver. During hour 2 or 3 of the insulin clamp, hepatic glucose output was assumed to be negligible. The primary storage of glucose occurs as glycogen, as the contribution of anaerobic glycolysis to lactate is minimal under euglycemic conditions. NOG metabolism during insulin stimulation was calculated from the difference between total body glucose disposal and glucose oxidation, as determined by indirect calorimetry (Shulman et al., 1990). 2.5. CSF analyses The CSF samples were obtained under medication-free conditions between 8 AM and 9 AM, after the subjects had been maintained on a low-monoamine diet for at least 1 week (Virkkunen et al., 1996). The concentration of the major metabolite of serotonin, 5-HIAA, was quantified by high-pressure liquid chromatography with electrochemical detection (HPLC-EC). Both the within-run and betweenrun coefficients of variation of the method were less than 10% (Scheinin et al., 1983). CSF analyses were performed by personnel who were unaware of both the sample identity and clinical characteristics of the subjects. 2.6. Statistical analysis The primary outcome variable was the legal status of the subject (i.e., having committed at least one recidivistic
offence during the follow-up; yes = 1, no = 0), and the main explanatory variable was NOG metabolism. Differences in numeric outcomes between healthy subjects, recidivistic subjects, and non-recidivistic subjects were analyzed using a one-way analysis of variance (ANOVA). The Bonferroni multiple-comparison test was used when ANOVA indicated a statistically significant difference. Analysis of covariance (ANCOVA) was used to verify NOG results when the outcome variables were adjusted for age, BMI, and S-gamma glutamyl transferase (SGGT). Logistic regression analysis was used to estimate how much of the recidivistic offending was attributable to NOG values. Receiver operating characteristics (ROC) analysis was used to study how accurately low NOG predicted recidivistic offending (NCSS 2004; http://www. ncss.com). Effect size (Cohen's d; Cohen, 1988) was used to describe the robustness of the findings. 3. Results During the follow-up, 32 subjects had not been arrested for any new violent offenses, and 17 had committed at least one new offense. The length of follow-up (i.e., the time spent in freedom after release from prison; mean± S.D.) among all released subjects was 25.6 ± 20.5 months (12.0 ± 8.7 among recidivistic offenders and 32.8 ± 21.4 among non-recidivistic offenders). The offender groups had higher S-GGT, S-ALAT, and S-ASAT values than healthy subjects, but there were no statistically significant differences between the healthy subjects, recidivistic subjects and non-recidivistic subjects for age, weight, or BMI (Table 1). The recidivistic offenders had higher baseline insulin levels than non-residivistic offenders and healthy subjects.IQ scores (as determined by the Wechsler Adult Intelligence Scale) did not differ between recidivistic (90.8 ± 10.0; n = 14) vs. non-recidivistic (94.4 ± 12.4; n = 32) offenders (F1,44 = 0.93, P = 0.34). NOG disposal was 1.4 to 1.6 pooled standard deviations lower (F2,86 = 15.7, P = 0.000002) among recidivistic subjects than either non-recidivistic subjects or healthy subjects; Table 1, Fig. 1). This result did not change when age, BMI, and S-GGT were adjusted by using ANCOVA (F2,80 = 11.3, P = 0.00005). No statistically significant differences were observed in stimulated glucose oxidation, stimulated lipid oxidation or CSF 5HIAA levels (Table 1). In the NOG ROC analysis, the area under curve was 0.85 (95% CI 0.69–0.93, P b 0.001; Fig. 2). None of the liver function indicators (S-GGT, SALAT, S-ASAT) predicted recidivistic offending (AUC ≤ 0.67, P N 0.05). In the logistic regression analysis including NOG, age, BMI and S-GGT (as an indicator of liver damage), NOG was the only statistically significant
M. Virkkunen et al. / Psychiatry Research 168 (2009) 26–31
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Table 1 Background and metabolic variables (mean ± S.D.) among recidivistic vs. non-recidivistic vs. healthy subjects.
Age (years) Weight (kg) BMI (kg/m2) S-ALAT S-ASAT S-GGT S-insulin (mU/l) S-glucagon (ng/l) S-glucose (mmol/l) S-FFA (mmol/l) NOG (mg/kg min) GOX (mg/kg min) LOX (mg/kg min) CSF 5-HIAA (mol/l)
Recidivistic offenders (n = 17)
Non-recidivistic offenders (controls) (n = 32)
Healthy subjects (n = 40)
Significance
32.3 ± 10.7 76.3 ± 12.5 24.2 ± 3.1 112.2 ± 158.0 106.4 ± 199.8 112.3 ± 170.0 15.0 ± 8.3 56.67 ± 19.25 4.1 ± 0.7 0.40 ± 0.22 2.40 ± 1.97 4.51 ± 0.93 0.18 ± 0.26 64.1 ± 26.9
32.9 ± 9.2 76.9 ± 8.8 24.4 ± 3.5 69.0 ± 106.5 62.6 ± 90.2 62.3 ± 108.6 9.7 ± 5.6 54.13 ± 21.3 4.0 ± 0.5 0.40 ± 0.26 5.64 ± 2.47 4.48 ± 0.73 0.14 ± 0.27 67.4 ± 25.6
33.7 ± 8.7 80.5 ± 11.3 24.9 ± 3.0 9.0 ± 6.8 26.1 ± 13.5 27.2 ± 24.4 9.1 ± 5.3 74.9 ± 30.3 3.9 ± 0.5 0.62 ± 0.28 6.49 ± 2.80 4.20 ± 1.05 0.28 ± 0.37 69.3 ± 25.6
F2,86 = 0.16, P = 0.86 F2,86 = 1.41, P = 0.25 F2,86 = 0.42, P = 0.66 F2,83 = 7.83, P = 0.001 F2,83 = 3.67, P = 0.03 F2,83 = 4.22, P = 0.02 F2,76 = 5.14, P = 0.008 F2.79 = 6.01, P = 0.004 F2,79 = 0.54, P = 0.59 F2,85 = 7.64, P = 0.001 F2,86 = 15.7, P = 0.000002 F2,86 = 1.08, P = 0.34 F2,84 = 1.72, P = 0.19 F2,47 = 0.11, P = 0.90
In Bonferroni Post Hoc tests concerning NOG values, recidivistic offenders differed from non-recidivistic offenders and healthy subjects (P b 0.001), whereas these two latter groups did not differ from each other (P N 0.05). Data on CSF 5-HIAA were available from only 6 recidivistic offenders, 11 non-recidivistic offenders and 33 healthy subjects. The median age (range) was 35.0 years (18–49) for recidivistic offenders, 34.5 years (18–47) for non-recidivistic offenders, and 31.5 years (22–60) for healthy subjects. BMI = body mass index, S-GGT = S-gamma glutamyl transferase, NOG = Non-oxidative glucose disposal, GOX = Stimulated glucose oxidation, LOX = Stimulated lipid oxidation, 5-HIAA = CSF 5-hydroxyindoleacetic acid.
factor explaining new violent offenses (P = 0.00004), and a substantial proportion (27%, r2 = 0.27) of recidivistic offending was explained by low NOG alone.
Fig. 1. A box-plot of non-oxidative glucose (NOG) metabolism comparing recidivistic offenders who had committed at least one new violent offense (left) with non-recidivistic subjects (middle) and healthy subjects (right). The mean NOG value of recidivistic offenders was 1.4 S. D. lower when compared with non-recidivistic offenders and 1.6. S.D. lower when compared with healthy subjects (F2,86 = 15.7, P = 0.000002; F2,80 = 11.3, P = 0.00005, age, BMI and S-GGT adjusted ANCOVA).
4. Discussion These results confirmed our hypothesis that low nonoxidative glucose (NOG) metabolism predicts violent offending among males having antisocial personality disorder. There was a strong relationship between low NOG and future violent behavior; i.e., those offenders who
Fig. 2. Receiver operating characteristics (ROC) analysis of nonoxidative glucose metabolism values to predict recidivist violent crimes among the offender population (n = 49). The area under curve is 0.85 (95% CI 0.69–0.93).
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committed at least one new violent crime had markedly lower NOG values than either healthy subjects (effect size 1.6) or those offenders who were not convicted of any new offense (effect size 1.4). To our knowledge, in addition to our previous findings on low CSF 5-HIAA and MHPG levels (Virkkunen et al., 1989, 1996), this finding is the only other biological variable that has been observed to predict the commission of violent crimes. The magnitude of the NOG finding is considerably more robust (i.e., effect size 1.4–1.6) than in our previous (effect size b 0.8; Virkkunen et al., 1994, 1996) or present (effect size b 0.2) results on CSF monoamine metabolite levels. A limitation of our study was that the participation rate in lumbar puncture among offenders included in this study was less than 40%, which may have led to a bias in which subgroups had different behavioral traits. However, this has also been the case in all previous CSF 5-HIAA studies on habitually violent offenders (Virkkunen et al., 1989, 1994, 1996) and, more importantly, the participation rate was identical among recidivistic vs. non-recidivistic offenders. We think that the findings on glucose and lipid metabolism are representative and can be generalized for all three groups, as none of those included in the study refused to participate in the insulin clamp and calorimetry measurements. In the ROC analysis, the area under curve was 0.85 which is, to our knowledge, the highest value reported thus far for any single biological variable in violence prediction. In the logistic regression analysis, NOG alone explained 27% of the variation in recidivistic offending. These results suggest that low non-oxidative metabolism may be a crucial component in the pathophysiology of habitually violent behavior among subjects having antisocial personality disorder. Our previous results indicate that a majority of all habitually violent offenders with APD have low NOG metabolism and low glycogen formation when compared with healthy subjects (Virkkunen et al., 2007), and it is believed that hypoglycemia is associated with aggressive behavior (Benton et al., 1982; Virkkunen et al., 1994). In addition, several imaging studies have shown decreased glucose uptake in the prefrontal cortex in violent offenders even in euglycemic state (Bufkin and Luttrell, 2005). Brain contains glycogen, a putative energy reserve in the brain, analogous to its role in the periphery: ethanol is known to produce hypoglycemia and it is believed that glycogen provides fuel to support brain function during pathological hypoglycemia, although the mechanisms triggering glycogen formation in CNS cells are not known at the moment (Benington and Heller, 1995; Brown, 2004). The issue of why some people become irritated and impulsively aggressive under the influence of alcohol remains to be clarified under laboratory conditions, while measuring
various metabolic and hormonal factors. However, such research is hampered by the fact that exposing habitually violent alcoholic offenders to ethanol is problematic from an ethical point of view. It is important to clarify in future studies whether or not increasing glycogen formation could decrease both the risk of hypoglycemia and low brain glucose metabolism and the tendency of habitually violent offending under the influence of alcohol in this population. It is also important to study how stress and hormones (such as cortisol, epinephrine and growth hormone) contribute to low non-oxidative glucose metabolism. Lithium, sodium valproate, and zinc ions have the ability to increase glycogen formation by affecting glycogen synthase kinase 3 beta (GSK3 beta) (Jope, 1999; DeSarno et al., 2002; Ilouz et al., 2002). The FDA (USA) has thus far approved only lithium as an antiaggressive medication in this regard. Lithium has diminished aggressive and violent behavior in hospitalized, severely aggressive children and adolescents with conduct disorder (which precedes APD) (Campbell et al., 1984; Malone et al., 2000), and in violent offenders with APD under prison conditions (Sheard et al., 1976). Also, sodium divalproex has improved self-reported impulse control and self-restraint in conduct disorder, which are variables that have been shown to be predictive of criminal recidivism (Steiner et al., 2003). In a recent double-blind, placebo-controlled, randomized trial, Gesch et al. (2002) gave nutritional supplements to 231 young adult prisoners and compared disciplinary offences before and during supplementation. This also included the daily recommended intake of zinc (11 mg). Those receiving active capsules committed on average 26% fewer offences while incarcerated. Thus, antisocial behavior, including violence, was reduced by dietary supplementation of vitamins, minerals and essential fatty acids. It is not clear, however, which constituent contributed most to these changes, although zinc ion is one possible candidate for causing the anti-aggressive effect. In conclusion, antisocial males who commit recidivistic offenses had markedly low non-oxidative glucose (NOG) metabolism. This finding explains a substantial proportion of forthcoming violent offending among antisocial males, and suggests that low non-oxidative metabolism may be a crucial component in the pathophysiology of habitually violent behavior among subjects with antisocial personality disorder. Acknowledgements This study was supported by Award/Contract N01AA53003 from the National Institute on Alcohol Abuse and Alcoholism, Rockville, MD, USA, and by
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funding from Annual EVO Financing (Special government subsidies). The authors thank Ms. Aija Räsänen for her secretarial assistance. References American Psychiatric Association, 1987. DSM-III-R: Diagnostic and Statistical Manual of Mental Disorders, 3rd edition, revised. The Association, Washington, DC. Benington, J.H., Heller, H.C., 1995. Restoration of brain energy metabolism as the function of sleep. Progress in Neurobiology 45, 347–360. Benton, D., Kumari, N., Brain, P.F., 1982. Mild hypoglycaemia and questionnaire measures of aggression. Biological Psychology 14, 129–135. Bonta, J., Law, M., Hanson, K., 1998. The prediction of criminal and violent recidivism among mentally disordered offenders: a metaanalysis. Psychological Bulletin 123, 123–142. Brown, A.M., 2004. Brain glycogen re-awakened. Journal of Neurochemistry 89, 537–552. Bufkin, J.L., Luttrell, V.R., 2005. Neuroimaging studies of aggressive and violent behaviour. Current findings and implications for criminology and criminal justice. Trauma, Violence and Abuse 6, 176–191. Campbell, M., Small, A.M., Green, W.H., Jennings, S.J., Perry, R., Bennett, W.G., Anderson, L., 1984. Behavioral efficacy of haloperidol and lithium carbonate. A comparison in hospitalized aggressive children with conduct disorder. Archives of General Psychiatry 41, 650–656. Cohen, J., 1988. Statistical Analysis for the Behavioral Sciences, 2nd ed. Lawrence Erlbaum Associates, Hillsdale, NJ. DeSarno, P., Li, X., Jope, R.S., 2002. Regulation of Akt and glycogen synthase kinase-3 beta phosphorylation by sodium valproate and lithium. Neuropharmacology 43, 1158–1164. Dolan, M., Doyle, M., 2000. Violence risk prediction. Clinical and actuarial measures and the role of the Psychopathy Checklist. British Journal of Psychiatry 177, 303–311. Eronen, M., Hakola, P., Tiihonen, J., 1996. Factors associated with homicide recidivism in a 13-year sample of homicide offenders in Finland. Psychiatric Services 47, 403–406. Ferrannini, E., 1988. The theoretical bases of indirect calorimetry: a review. Metabolism 37, 287–301. Franssila-Kallunki, A., Schalin-Jantti, C., Groop, L., 1992. Effect of gender on insulin resistance associated with aging. American Journal of Physiology — Endocrinology and Metabolism 263, E780–E785. Franssila-Kallunki, A., 1994. Energy Metabolism in Insulin Resistant States. (Academic Dissertation) Helsinki University Yliopistopaino, Helsinki, Finland. Gesch, C.B., Hammond, S.M., Hampson, S.E., Eves, A., Crowder, M.J., 2002. Influence of supplementary vitamins, minerals and essential fatty acids on the antisocial behavior of young adult prisoners. Randomized placebo-controlled trial. British Journal of Psychiatry 181, 22–28.
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Psychiatry Research 168 (2009) 26 – 31 www.elsevier.com/locate/psychres
Low non-oxidative glucose metabolism and violent offending: An 8-year prospective follow-up study Matti Virkkunen a,⁎, Aila Rissanen a , Anja Franssila-Kallunki a , Jari Tiihonen b,c a
Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland b Department of Forensic Psychiatry, University of Kuopio, Kuopio, Finland c Department of Clinical Physiology, Kuopio University Hospital, Kuopio; Finland
Received 11 October 2006; received in revised form 30 July 2007; accepted 22 March 2008
Abstract Violent offenders have abnormalities in their glucose metabolism as indicated by decreased glucose uptake in their prefrontal cortex and a low blood glucose nadir in the glucose tolerance test. We tested the hypothesis that low non-oxidative glucose metabolism (NOG) predicts forthcoming violent offending among antisocial males. Glucose metabolism was measured using the insulin clamp method among 49 impulsive, violent, antisocial offenders during a forensic psychiatric examination. Those offenders who committed at least one new violent crime during the 8-year follow-up had a mean NOG of 1.4 standard deviations lower than non-recidivistic offenders. In logistic regression analysis, NOG alone explained 27% of the variation in the recidivistic offending. Low non-oxidative metabolism may be a crucial component in the pathophysiology of habitually violent behavior among subjects with antisocial personality disorder. This might suggest that substances increasing glycogen formation and decreasing the risk of hypoglycemia might be potential treatments for impulsive violent behavior. © 2008 Elsevier Ireland Ltd. All rights reserved. Keywords: Aggression; Alcohol abuse; Forensic psychiatry; Metabolism; Personality disorders; Violence
1. Introduction A large proportion of violent offenses in Western countries are attributable to antisocial personality disorder (APD), which is associated with early onset alcoholism. In Finland, for example, offenders with APD commit up to 60–80% of the most severe recidivist violent offenses (Tiihonen and Hakola, 1994; Eronen
⁎ Corresponding author. Department of Psychiatry, Helsinki University Central Hospital, PO Box 320, FI-00029 HUCH, Helsinki, Finland. Tel.: +358 9 471 81 260; fax: +358 9 471 81 313. E-mail address: [email protected] (M. Virkkunen).
et al., 1996). Risk assessment of violent offending among this population has been considered to be problematic and of little practical value (Dolan and Doyle, 2000). Variables in criminal history, or actual measures of criminological variables, have been the best predictors, whereas clinical variables have provided the smallest effect sizes (Bonta et al., 1998; Gray et al., 2004). The only biological factors that have predicted violent offending in prospective follow-up studies have been low cerebrospinal fluid 5-hydroxyindoleacetic acid (CSF 5-HIAA) and low CSF 3-methoxy-4-hydroxyphenylglycol (MHPG), which are metabolites of the neurotransmitters serotonin and norepinephrine, respectively (Virkkunen et al., 1989, 1996).
0165-1781/$ - see front matter © 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.psychres.2008.03.026
M. Virkkunen et al. / Psychiatry Research 168 (2009) 26–31
In a recent study using the insulin clamp/calorimetry method, we found that habitually violent male offenders with APD have very low non-oxidative glucose (NOG) metabolism (i.e., glycogen formation) when compared with normal male controls that were matched for both age and body mass index (BMI; Virkkunen et al., 2007). The aim of this prospective follow-up study was to test the hypothesis that low NOG metabolism is a trait that predicts recidivistic violent offending among habitually violent antisocial males. 2. Methods and materials 2.1. Protocol Informed consent was obtained from all participants after the procedure had been fully explained, and consideration for the appropriate protection of human rights was observed throughout the study. The study was approved by the Ethical Committee of the Helsinki University Central Hospital, Finland. Incarcerated subjects had the option of contacting a Finnish prison representative during the study. 2.2. Diagnostic and psychosocial assessment All subjects, including healthy subjects, were assessed with the Structured Clinical Interview for DSM-III-R (SCID, axes I and II; American Psychiatric Association, 1987). 2.3. Subjects This study included 69 habitually violent offenders who were admitted to a forensic psychiatric examination on a closed ward, and who also fulfilled the DSM-III-R criteria for APD and alcohol dependence (for additional details on co-morbidity, see Virkkunen et al., 2007). A diagnosis of psychosis was an exclusion criterion. Two subjects were later omitted because of their poor performance on an intelligence test (WAIS IQ 60–70). The subjects represented typical violent alcoholic offenders who had recently committed severe violent crimes (homicide, attempted homicide, aggravated assault, assault) and who had, on average, been incarcerated in prison for 3–6 months before examination. In the forensic mental examination, all the crimes were considered to be impulsive (not premeditated), and the offenses were committed under the influence of alcohol or illicit drugs in all 67 cases. This group did not have access to alcohol at least 3 months prior to attending the forensic mental examination and subsequent insulin clamp calorimetry at the Department of Psychiatry, Helsinki University Hospital. During the last week before
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insulin clamp and calorimetry measures, the prisoners were found to be completely drug- and medication-free on the day of arrival to the Forensic Psychiatric Department, as confirmed by urinalysis. During their week at the unit, the prisoners were allowed to move freely throughout the ward. All subjects ate the same food during the 24-hour period preceding the measurements. The initial insulin clamp with indirect calorimetry and other biological measurements were made after the first wash-out week, without medication or intensive physical training. However, none of the study subjects had used antidepressants or antipsychotics within the last 2 weeks prior to the insulin clamp measurement. During the pre-hospital period in prison, inmates had an opportunity to engage in physical training if they wished. All 67 subjects were followed for up to 8 years by using the Finnish central prison register to observe the duration of incarceration, and by using the national crime register database to identify and observe recidivist offenses after release from prison. Five subjects died during the follow-up period, 13 subjects remained incarcerated (and were not included in the analysis), and 49 subjects were released until the end of the follow-up period (December 2003). The endpoints for the follow-up were either the end of the follow-up period or a new incarceration in prison as a result of a recidive offence. All subjects who were eligible for the study participated in the insulin clamp and calorimetry measurements, whereas only 6 (35%) of the 17 recidivistic offenders and 11 (34%) of the 32 non-recidivistic offenders (Χ2 =0.00, P = 0.95) were willing to participate in the lumbar puncture (i.e., 5-HIAA measurements). Thirty-three (83%) of the 40 healthy subjects agreed to lumbar puncture. The results were contrasted with data obtained from healthy subjects (n=40), who were matched for both age and BMI (see Virkkunen et al., 2007), with the same procedure, and during the same period. The volunteers (students, nurses, policemen, firemen, postmen) were recruited through advertisements. In the advertisement, inclusion criteria were male gender, an age of more than 18 years and normal weight. No formal upper limits for age and weight were set in the advertisement. After the mean age and weight of the offenders started to emerge during the study, the healthy subjects were selected from the pool of volunteers to match the characteristics of the offenders. The advertisement further required that the volunteers had to be free of current or past drinking problems and mental disorders. 2.4. Euglycemic insulin clamp A 3-hour euglycemic, hyperinsulinemic clamp combined with indirect calorimetry was performed on all participants to estimate glucose oxidation (GOX), non-
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oxidative glucose metabolism (NOG) and lipid oxidation (LOX) while they remained in a recumbent position, beginning at 7:30 AM after a 12-hour overnight fast. These values were calculated on the basis of fat-free mass. Body fat and fat-free mass were assessed by bioelectrical impedance. Subjects received no medications prior to the study. After basal samples had been obtained, a primed continuous infusion of short-acting human insulin (Actrapid Human, Novo Nordisk, Copenhagen, Denmark) was administered at a rate of 45 mU/m2/min (ca. 340 pmol/m2/min) for 3 h. Plasma glucose concentration was determined at 5-min intervals, and 20% glucose was infused to maintain a constant plasma glucose concentration (see Franssila-Kallunki et al., 1992). Indirect calorimetry was analyzed in the basal state and after 150–180 min (Ferrannini, 1988) with a computerized, open-circuit system (Deltatrac, Datex, Helsinki, Finland) to measure gas exchange rates (i.e., CO2 production and O2 consumption; Meriläinen, 1987). The monitor had a precision of 2.6% for oxygen consumption and 1% for carbon dioxide production (Franssila-Kallunki, 1994). The amount of glucose that is required to maintain euglycemia is equivalent to the whole-body disposal of glucose (M-value), provided that there is no entry of glucose from the liver. During hour 2 or 3 of the insulin clamp, hepatic glucose output was assumed to be negligible. The primary storage of glucose occurs as glycogen, as the contribution of anaerobic glycolysis to lactate is minimal under euglycemic conditions. NOG metabolism during insulin stimulation was calculated from the difference between total body glucose disposal and glucose oxidation, as determined by indirect calorimetry (Shulman et al., 1990). 2.5. CSF analyses The CSF samples were obtained under medication-free conditions between 8 AM and 9 AM, after the subjects had been maintained on a low-monoamine diet for at least 1 week (Virkkunen et al., 1996). The concentration of the major metabolite of serotonin, 5-HIAA, was quantified by high-pressure liquid chromatography with electrochemical detection (HPLC-EC). Both the within-run and betweenrun coefficients of variation of the method were less than 10% (Scheinin et al., 1983). CSF analyses were performed by personnel who were unaware of both the sample identity and clinical characteristics of the subjects. 2.6. Statistical analysis The primary outcome variable was the legal status of the subject (i.e., having committed at least one recidivistic
offence during the follow-up; yes = 1, no = 0), and the main explanatory variable was NOG metabolism. Differences in numeric outcomes between healthy subjects, recidivistic subjects, and non-recidivistic subjects were analyzed using a one-way analysis of variance (ANOVA). The Bonferroni multiple-comparison test was used when ANOVA indicated a statistically significant difference. Analysis of covariance (ANCOVA) was used to verify NOG results when the outcome variables were adjusted for age, BMI, and S-gamma glutamyl transferase (SGGT). Logistic regression analysis was used to estimate how much of the recidivistic offending was attributable to NOG values. Receiver operating characteristics (ROC) analysis was used to study how accurately low NOG predicted recidivistic offending (NCSS 2004; http://www. ncss.com). Effect size (Cohen's d; Cohen, 1988) was used to describe the robustness of the findings. 3. Results During the follow-up, 32 subjects had not been arrested for any new violent offenses, and 17 had committed at least one new offense. The length of follow-up (i.e., the time spent in freedom after release from prison; mean± S.D.) among all released subjects was 25.6 ± 20.5 months (12.0 ± 8.7 among recidivistic offenders and 32.8 ± 21.4 among non-recidivistic offenders). The offender groups had higher S-GGT, S-ALAT, and S-ASAT values than healthy subjects, but there were no statistically significant differences between the healthy subjects, recidivistic subjects and non-recidivistic subjects for age, weight, or BMI (Table 1). The recidivistic offenders had higher baseline insulin levels than non-residivistic offenders and healthy subjects.IQ scores (as determined by the Wechsler Adult Intelligence Scale) did not differ between recidivistic (90.8 ± 10.0; n = 14) vs. non-recidivistic (94.4 ± 12.4; n = 32) offenders (F1,44 = 0.93, P = 0.34). NOG disposal was 1.4 to 1.6 pooled standard deviations lower (F2,86 = 15.7, P = 0.000002) among recidivistic subjects than either non-recidivistic subjects or healthy subjects; Table 1, Fig. 1). This result did not change when age, BMI, and S-GGT were adjusted by using ANCOVA (F2,80 = 11.3, P = 0.00005). No statistically significant differences were observed in stimulated glucose oxidation, stimulated lipid oxidation or CSF 5HIAA levels (Table 1). In the NOG ROC analysis, the area under curve was 0.85 (95% CI 0.69–0.93, P b 0.001; Fig. 2). None of the liver function indicators (S-GGT, SALAT, S-ASAT) predicted recidivistic offending (AUC ≤ 0.67, P N 0.05). In the logistic regression analysis including NOG, age, BMI and S-GGT (as an indicator of liver damage), NOG was the only statistically significant
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Table 1 Background and metabolic variables (mean ± S.D.) among recidivistic vs. non-recidivistic vs. healthy subjects.
Age (years) Weight (kg) BMI (kg/m2) S-ALAT S-ASAT S-GGT S-insulin (mU/l) S-glucagon (ng/l) S-glucose (mmol/l) S-FFA (mmol/l) NOG (mg/kg min) GOX (mg/kg min) LOX (mg/kg min) CSF 5-HIAA (mol/l)
Recidivistic offenders (n = 17)
Non-recidivistic offenders (controls) (n = 32)
Healthy subjects (n = 40)
Significance
32.3 ± 10.7 76.3 ± 12.5 24.2 ± 3.1 112.2 ± 158.0 106.4 ± 199.8 112.3 ± 170.0 15.0 ± 8.3 56.67 ± 19.25 4.1 ± 0.7 0.40 ± 0.22 2.40 ± 1.97 4.51 ± 0.93 0.18 ± 0.26 64.1 ± 26.9
32.9 ± 9.2 76.9 ± 8.8 24.4 ± 3.5 69.0 ± 106.5 62.6 ± 90.2 62.3 ± 108.6 9.7 ± 5.6 54.13 ± 21.3 4.0 ± 0.5 0.40 ± 0.26 5.64 ± 2.47 4.48 ± 0.73 0.14 ± 0.27 67.4 ± 25.6
33.7 ± 8.7 80.5 ± 11.3 24.9 ± 3.0 9.0 ± 6.8 26.1 ± 13.5 27.2 ± 24.4 9.1 ± 5.3 74.9 ± 30.3 3.9 ± 0.5 0.62 ± 0.28 6.49 ± 2.80 4.20 ± 1.05 0.28 ± 0.37 69.3 ± 25.6
F2,86 = 0.16, P = 0.86 F2,86 = 1.41, P = 0.25 F2,86 = 0.42, P = 0.66 F2,83 = 7.83, P = 0.001 F2,83 = 3.67, P = 0.03 F2,83 = 4.22, P = 0.02 F2,76 = 5.14, P = 0.008 F2.79 = 6.01, P = 0.004 F2,79 = 0.54, P = 0.59 F2,85 = 7.64, P = 0.001 F2,86 = 15.7, P = 0.000002 F2,86 = 1.08, P = 0.34 F2,84 = 1.72, P = 0.19 F2,47 = 0.11, P = 0.90
In Bonferroni Post Hoc tests concerning NOG values, recidivistic offenders differed from non-recidivistic offenders and healthy subjects (P b 0.001), whereas these two latter groups did not differ from each other (P N 0.05). Data on CSF 5-HIAA were available from only 6 recidivistic offenders, 11 non-recidivistic offenders and 33 healthy subjects. The median age (range) was 35.0 years (18–49) for recidivistic offenders, 34.5 years (18–47) for non-recidivistic offenders, and 31.5 years (22–60) for healthy subjects. BMI = body mass index, S-GGT = S-gamma glutamyl transferase, NOG = Non-oxidative glucose disposal, GOX = Stimulated glucose oxidation, LOX = Stimulated lipid oxidation, 5-HIAA = CSF 5-hydroxyindoleacetic acid.
factor explaining new violent offenses (P = 0.00004), and a substantial proportion (27%, r2 = 0.27) of recidivistic offending was explained by low NOG alone.
Fig. 1. A box-plot of non-oxidative glucose (NOG) metabolism comparing recidivistic offenders who had committed at least one new violent offense (left) with non-recidivistic subjects (middle) and healthy subjects (right). The mean NOG value of recidivistic offenders was 1.4 S. D. lower when compared with non-recidivistic offenders and 1.6. S.D. lower when compared with healthy subjects (F2,86 = 15.7, P = 0.000002; F2,80 = 11.3, P = 0.00005, age, BMI and S-GGT adjusted ANCOVA).
4. Discussion These results confirmed our hypothesis that low nonoxidative glucose (NOG) metabolism predicts violent offending among males having antisocial personality disorder. There was a strong relationship between low NOG and future violent behavior; i.e., those offenders who
Fig. 2. Receiver operating characteristics (ROC) analysis of nonoxidative glucose metabolism values to predict recidivist violent crimes among the offender population (n = 49). The area under curve is 0.85 (95% CI 0.69–0.93).
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committed at least one new violent crime had markedly lower NOG values than either healthy subjects (effect size 1.6) or those offenders who were not convicted of any new offense (effect size 1.4). To our knowledge, in addition to our previous findings on low CSF 5-HIAA and MHPG levels (Virkkunen et al., 1989, 1996), this finding is the only other biological variable that has been observed to predict the commission of violent crimes. The magnitude of the NOG finding is considerably more robust (i.e., effect size 1.4–1.6) than in our previous (effect size b 0.8; Virkkunen et al., 1994, 1996) or present (effect size b 0.2) results on CSF monoamine metabolite levels. A limitation of our study was that the participation rate in lumbar puncture among offenders included in this study was less than 40%, which may have led to a bias in which subgroups had different behavioral traits. However, this has also been the case in all previous CSF 5-HIAA studies on habitually violent offenders (Virkkunen et al., 1989, 1994, 1996) and, more importantly, the participation rate was identical among recidivistic vs. non-recidivistic offenders. We think that the findings on glucose and lipid metabolism are representative and can be generalized for all three groups, as none of those included in the study refused to participate in the insulin clamp and calorimetry measurements. In the ROC analysis, the area under curve was 0.85 which is, to our knowledge, the highest value reported thus far for any single biological variable in violence prediction. In the logistic regression analysis, NOG alone explained 27% of the variation in recidivistic offending. These results suggest that low non-oxidative metabolism may be a crucial component in the pathophysiology of habitually violent behavior among subjects having antisocial personality disorder. Our previous results indicate that a majority of all habitually violent offenders with APD have low NOG metabolism and low glycogen formation when compared with healthy subjects (Virkkunen et al., 2007), and it is believed that hypoglycemia is associated with aggressive behavior (Benton et al., 1982; Virkkunen et al., 1994). In addition, several imaging studies have shown decreased glucose uptake in the prefrontal cortex in violent offenders even in euglycemic state (Bufkin and Luttrell, 2005). Brain contains glycogen, a putative energy reserve in the brain, analogous to its role in the periphery: ethanol is known to produce hypoglycemia and it is believed that glycogen provides fuel to support brain function during pathological hypoglycemia, although the mechanisms triggering glycogen formation in CNS cells are not known at the moment (Benington and Heller, 1995; Brown, 2004). The issue of why some people become irritated and impulsively aggressive under the influence of alcohol remains to be clarified under laboratory conditions, while measuring
various metabolic and hormonal factors. However, such research is hampered by the fact that exposing habitually violent alcoholic offenders to ethanol is problematic from an ethical point of view. It is important to clarify in future studies whether or not increasing glycogen formation could decrease both the risk of hypoglycemia and low brain glucose metabolism and the tendency of habitually violent offending under the influence of alcohol in this population. It is also important to study how stress and hormones (such as cortisol, epinephrine and growth hormone) contribute to low non-oxidative glucose metabolism. Lithium, sodium valproate, and zinc ions have the ability to increase glycogen formation by affecting glycogen synthase kinase 3 beta (GSK3 beta) (Jope, 1999; DeSarno et al., 2002; Ilouz et al., 2002). The FDA (USA) has thus far approved only lithium as an antiaggressive medication in this regard. Lithium has diminished aggressive and violent behavior in hospitalized, severely aggressive children and adolescents with conduct disorder (which precedes APD) (Campbell et al., 1984; Malone et al., 2000), and in violent offenders with APD under prison conditions (Sheard et al., 1976). Also, sodium divalproex has improved self-reported impulse control and self-restraint in conduct disorder, which are variables that have been shown to be predictive of criminal recidivism (Steiner et al., 2003). In a recent double-blind, placebo-controlled, randomized trial, Gesch et al. (2002) gave nutritional supplements to 231 young adult prisoners and compared disciplinary offences before and during supplementation. This also included the daily recommended intake of zinc (11 mg). Those receiving active capsules committed on average 26% fewer offences while incarcerated. Thus, antisocial behavior, including violence, was reduced by dietary supplementation of vitamins, minerals and essential fatty acids. It is not clear, however, which constituent contributed most to these changes, although zinc ion is one possible candidate for causing the anti-aggressive effect. In conclusion, antisocial males who commit recidivistic offenses had markedly low non-oxidative glucose (NOG) metabolism. This finding explains a substantial proportion of forthcoming violent offending among antisocial males, and suggests that low non-oxidative metabolism may be a crucial component in the pathophysiology of habitually violent behavior among subjects with antisocial personality disorder. Acknowledgements This study was supported by Award/Contract N01AA53003 from the National Institute on Alcohol Abuse and Alcoholism, Rockville, MD, USA, and by
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