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Published online by Cambridge University Press:  02 January 2018

J. H. Thakore
Affiliation:
Neuroscience Centre, St Vincent's Hospital, Richmond Road, Dublin 3, Ireland. E-mail: j.thakore@rcsi.ie
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Abstract

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Copyright © 2006 The Royal College of Psychiatrists 

Critical methodological differences between the studies of Zhang et al (2004) and Ryan et al (2004) might explain why the two fail to agree. Zhang et al reported a standard deviation of 50 for the age of the controls, indicating that some were elderly; moreover the groups were not matched for gender. This is important as elderly males have higher amounts of intra-abdominal fat (IAF). Life-style parameters such as diet, exercise, smoking and alcohol intake were not measured or indeed compared between the two groups. Furthermore, we are not given any indication as to how an individual was selected for scanning, as not all of the controls and patients recruited had a magnetic resonance imaging (MRI) scan. The authors did not use the same scanning techniques as Seidell et al (Reference Seidell, Bakker and van der Kooy1990), who were among the first to describe the single-slice technique for estimating IAF area. There were large differences in terms of inversion and repetition times. Moreover, the most critical aspect of using a single scan to estimate IAF is to ensure that the scan is taken at the level of L4/L5 vertebra, which is best located by a radiological lateral scout and not palpation as performed by Zhang et al. Furthermore, MRI is not a ‘precise and reliable means of determining the two fat measures with better resolution than computed tomography’, as it can erroneously estimate the amount of IAF by 20%.

From a statistical perspective, a one-way ANOVA should have been used to compare any differences between the three groups, as the use of multiple t-tests might have led to a type 1 error. A ‘non-fasting glucose’ level is not a standardised measure and is therefore meaningless. The actual values for fasting glucose decreased in both male and female patients, and fasting insulin levels decreased in females following treatment. Therefore, what Zhang et al show is that treatment with these two antipsychotics improves the metabolic profile of their patients despite an alleged increase in IAF.

Koro et al (2002) claim that olanzapine is associated with a higher risk of developing type 2 diabetes than risperidone, but this is difficult to interpret because Table 1 in their paper clearly indicates that the number of new cases of diabetes is greater in patients on risperidone (5.1%) than olanzapine (2.0%). There is little doubt that antipsychotics contribute to the development of type 2 diabetes in patients with schizophrenia. What is questionable is the magnitude of this effect. To date, the attributable risk for such an effect ranges between 2.03% for clozapine, 0.8% for quetiapine, 0.63% for olanzapine and 0.05% for risperidone (Reference Leslie and RosenheckLeslie & Rosenheck, 2004).

Despite the evidence presented the debate still centres on the diabetogenic effects of certain atypical antipsychotics. The purpose of the editorial was to put these issues into perspective to ensure that patients with schizophrenia, irrespective of their prescribed medication, would be offered screening for both diabetes and the metabolic syndrome.

References

Leslie, D. L. & Rosenheck, R. A. (2004) Incidence of newly diagnosed diabetes attributable to atypical antipsychotic medications. American Journal of Psychiatry, 161, 17091711.Google Scholar
Seidell, J. C., Bakker, C. J. & van der Kooy, K. (1990) Imaging techniques for measuring adipose-tissue distribution – a comparison between computed tomography and 1.5-T magnetic resonance. American Journal of Clinical Nutrition, 51, 953957.Google Scholar
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