Effect of Treatment on Weight Gain and Metabolic Abnormalities in Patients with First-Episode Psychosis

Verma, Swapna; Liew, Alvin; Subramaniam, Mythily; Poon, Lye Yin

doi:10.1080/00048670903107609

Cited by 38 publications

(36 citation statements)

References 27 publications

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“…However, 7% weight gain is a marker for clinically significant weight change. 14,16–18 Additionally, we were unable to include in our analysis some key known correlates of weight gain, such as diet, exercise, and social support status. 62,63 We may also have sample selection bias; people who did not report their weight and thus were excluded from the study (n=50) may differ in important ways from those who reported their weight.…”

Section: Discussionmentioning

confidence: 99%

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Clinically Significant Weight Gain 1 Year After Occupational Back Injury

Keeney

Fulton‐Kehoe

Wickizer

et al. 2013

Journal of Occupational &Amp; Environmental Medicine

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Objective To examine the incidence of clinically significant weight gain one year after occupational back injury, and risk factors for that gain. Methods A cohort of Washington State workers with wage-replacement benefits for back injuries completed baseline and 1-year follow-up telephone interviews. We obtained additional measures from claims and medical records. Results Among 1,263 workers, 174 (13.8%) reported clinically significant weight gain (≥7%) 1 year after occupational back injury. Women and workers who had >180 days on wage replacement at 1 year were twice as likely (adjusted OR=2.17, 95% CI=1.54–3.07; adjusted OR=2.40, 95% CI=1.63–3.53, respectively; both P<0.001) to have clinically significant weight gain. Conclusions Women and workers on wage replacement >180 days may be susceptible to clinically significant weight gain following occupational back injury.

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Section: Discussionmentioning

confidence: 99%

“…14, 16 – 18 Definitions of clinically significant weight change are not consistent in the literature. Weight changes of any, 7 3%, 27 and 5% have also been used, 12, 13, 28 but we chose the more conservative measure of a 7% gain.…”

Section: Methodsmentioning

confidence: 99%

Clinically Significant Weight Gain 1 Year After Occupational Back Injury

Keeney

Fulton‐Kehoe

Wickizer

et al. 2013

Journal of Occupational &Amp; Environmental Medicine

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“…In the majority of cases, females have been found to have a higher prevalence of AAPD-induced weight gain (Aichhorn et al 2007;Haack et al 2009;Hakko et al 2006;Verma et al 2009). However, exceptions exist, where men have increased weight gain (Basson et al 2001) or no gender bias has been apparent (Lee et al 2004).…”

Section: Introductionmentioning

confidence: 97%

Gender-dependent consequences of chronic olanzapine in the rat: effects on body weight, inflammatory, metabolic and microbiota parameters

et al. 2012

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“…Inclusion criteria determined the selection of records which contained a diagnosis of schizophrenia (295.0, 295.10, 295.30, 295.90), schizoaffective disorder (295.70), delusional disorder (297.1) and psychotic disorder (298.90), [24] n = 214. Exclusion criteria included all cases with a secondary diagnosis of development or intellectual disability, those with a diagnosis of dementia or records that had missing biometric or lab data, leaving, n = 89.…”

Section: Methodsmentioning

confidence: 99%

A simple screening tool for metabolic syndrome risk in the psychiatric patient treated with antipsychotic medication

Patch

Knight²,

Phillips-McEnany³

et al. 2015

Clinical Nursing Studies

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Objective: This study sought to determine whether waist to height ratio (WhtR) can be used to identify psychiatric patients at risk for metabolic syndrome (MetS). Body mass index (BMI) is most often used as the primary determinant for MetS in current screening protocols. There is currently no defined range of values of BMI that determine the level of risk. Psychiatric patients have twice the risk of dying from cardiovascular events than the general population. Cardiovascular disease has been a consequence of metabolic dysregulation that is common to MetS which involves abnormalities in at least three of the following biometric parameters: increase waist circumference, decreased high density lipids (HDL), increased triglycerides (TRG), elevated fasting blood sugar (FBS) and elevated blood pressure (BP). It is well documented that antipsychotic medication is a risk factor for developing MetS.Methods: A retrospective record review (n = 48), evaluated the clinic records of psychiatric patients treated with antipsychotic medication. Data from these records compared the relationship between the WhtR and BMI. The association between WhtR and BMI was analyzed through a Pearson correlation analysis and their respectively sensitivity and specificity for predicting risk for MetS was analyzed using a Receiver Operating Characteristic (ROC) curve. Confounding of the relationships between WhtR and risk of MetS by other risk factors was examined using generalized linear models.Results: Pearson coefficients of BMI and WhtR revealed a strong positive correlation r = 0.935, p = .01. Furthermore, WhtR was evaluated utilizing a ROC curve and demonstrated maximum sensitivity between 0.52 and 0.58 with the maximum specificity at 78%. Both WhtR and BMI showed robust measures in determining metabolic syndrome risk. The relationship between WhtR and risk of MetS existed even when controlling for other factors. Conclusions:The ratio of waist circumference to height, can provide a reliable low-cost, non-invasive form of measurement that can be performed in any setting, to monitor the progression of metabolic involvement.

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Effect of Treatment on Weight Gain and Metabolic Abnormalities in Patients with First-Episode Psychosis

Cited by 38 publications

References 27 publications

Clinically Significant Weight Gain 1 Year After Occupational Back Injury

Clinically Significant Weight Gain 1 Year After Occupational Back Injury

Gender-dependent consequences of chronic olanzapine in the rat: effects on body weight, inflammatory, metabolic and microbiota parameters

A simple screening tool for metabolic syndrome risk in the psychiatric patient treated with antipsychotic medication

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