Literature DB >> 21062616

Use of clinical markers to identify metabolic syndrome in antipsychotic-treated patients.

Hua Jin1, Jonathan Meyer, Sunder Mudaliar, Robert Henry, Srikrishna Khandrika, Danielle K Glorioso, Helena Kraemer, Dilip Jeste.   

Abstract

OBJECTIVE: Metabolic syndrome (MetS) is prevalent among antipsychotic-treated patients; however, in psychiatric clinics, scarce resources often limit the feasibility of monitoring all 5 criteria that are necessary for diagnosing MetS. As one goal of the MetS definition is to facilitate the clinical identification of insulin-resistant individuals, other biomarkers of insulin resistance have been explored. However, there are relatively few data from antipsychotic-treated patients, especially on the association between these markers and the clinical MetS diagnosis.
METHOD: We analyzed data from 196 psychiatric patients over age 40 years enrolled in an ongoing study of antipsychotic-related metabolic effects that began in August 2005. In addition to anthropometric measures and MetS criteria, levels of certain metabolism-related peptides (ghrelin, adiponectin, peptide YY, leptin, and insulin) were measured. The utility of these clinical and metabolic markers to identify individuals with MetS was evaluated by constructing receiver operating characteristic curves. Optimal cutoff values were calculated for markers with the greatest area under the curve on the basis of sensitivities and specificities for MetS diagnosis.
RESULTS: Ninety-nine subjects (50.5%) met MetS criteria. The receiver operating characteristic analysis found that waist circumference, triglyceride to high-density lipoprotein (TG:HDL) ratio, and body mass index had the greatest area under the curve. The waist circumference cutoff value of 40 inches, TG:HDL ratio of 2.6, and body mass index of 28 kg/m² yielded sensitivities and specificities of 73% and 80%, 74% and 78%, and 75% and 74%, respectively, for MetS diagnosis.
CONCLUSIONS: Waist circumference, TG:HDL cholesterol ratio, or body mass index could be used as screens for identifying possible MetS in antipsychotic-treated patients to prompt complete investigation into all MetS criteria. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00245206. © Copyright 2010 Physicians Postgraduate Press, Inc.

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Year:  2010        PMID: 21062616     DOI: 10.4088/JCP.09m05414yel

Source DB:  PubMed          Journal:  J Clin Psychiatry        ISSN: 0160-6689            Impact factor:   4.384


  9 in total

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Authors:  Ellen E Lee; Averria Sirkin Martin; Christopher N Kaufmann; Jinyuan Liu; Julie Kangas; Rebecca E Daly; Xin M Tu; Colin A Depp; Dilip V Jeste
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2.  A 52-week, double-blind evaluation of the metabolic effects of aripiprazole and lithium in bipolar I disorder.

Authors:  Roger S McIntyre; Susan L McElroy; James M Eudicone; Robert A Forbes; Berit X Carlson; Ross A Baker
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Review 3.  New wine in old bottle: late-life psychosis.

Authors:  Alana Iglewicz; Thomas W Meeks; Dilip V Jeste
Journal:  Psychiatr Clin North Am       Date:  2011-06

4.  Antipsychotic-induced changes in blood levels of leptin in schizophrenia: a meta-analysis.

Authors:  Stéphane Potvin; Simon Zhornitsky; Emmanuel Stip
Journal:  Can J Psychiatry       Date:  2015-03       Impact factor: 4.356

Review 5.  Detection of metabolic syndrome in schizophrenia and implications for antipsychotic therapy : is there a role for folate?

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Journal:  Mol Diagn Ther       Date:  2013-02       Impact factor: 4.074

6.  Reliability and practicality of measuring waist circumference to monitor cardiovascular risk among community mental health center patients.

Authors:  Jessica Barber; Laura Palmese; Lydia A Chwastiak; Joseph C Ratliff; Erin L Reutenauer; Michel Jean-Baptiste; Cenk Tek
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Review 7.  Making Sense of Blood-Based Proteomics and Metabolomics in Psychiatric Research.

Authors:  Paul C Guest; Francesca L Guest; Daniel Martins-de Souza
Journal:  Int J Neuropsychopharmacol       Date:  2015-12-30       Impact factor: 5.176

8.  Clinical Correlates of Insulin Resistance in Chronic Schizophrenia: Relationship to Negative Symptoms.

Authors:  Virawudh Soontornniyomkij; Ellen E Lee; Hua Jin; Averria Sirkin Martin; Rebecca E Daly; Jinyuan Liu; Xin M Tu; Lisa Todd Eyler; Dilip V Jeste
Journal:  Front Psychiatry       Date:  2019-04-23       Impact factor: 4.157

Review 9.  Proteomic profiling in schizophrenia: enabling stratification for more effective treatment.

Authors:  Paul C Guest; Daniel Martins-de-Souza; Emanuel Schwarz; Hassan Rahmoune; Murtada Alsaif; Jakub Tomasik; Christoph W Turck; Sabine Bahn
Journal:  Genome Med       Date:  2013-03-26       Impact factor: 11.117

  9 in total

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