Screening for the metabolic syndrome in patients receiving antipsychotic treatment: a proposed algorithm

Waterreus, Anna; Laugharne, Jonathan

doi:10.5694/j.1326-5377.2009.tb02344.x

Cited by 45 publications

(44 citation statements)

References 22 publications

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“…Kidney failure is clearly a medical phenotype, rather than a psychiatric phenotype, but this network demonstrates genetic influences on the comorbidity and on this medical phenotype [86,87]. Consistent with this observation, many genes that are expressed in the brain are expressed in other tissues, so variation that influences psychiatric disorders may have whole body effects and influence what are traditionally considered medical disorders [88-90], thus blurring the boundaries between psychiatric and medical conditions.…”

Section: Discussionmentioning

confidence: 97%

Modeling complex genetic and environmental influences on comorbid bipolar disorder with tobacco use disorder

McEachin

Saccone

et al. 2010

BMC Med Genet

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BackgroundComorbidity of psychiatric and substance use disorders represents a significant complication in the clinical course of both disorders. Bipolar Disorder (BD) is a psychiatric disorder characterized by severe mood swings, ranging from mania to depression, and up to a 70% rate of comorbid Tobacco Use Disorder (TUD). We found epidemiological evidence consistent with a common underlying etiology for BD and TUD, as well as evidence of both genetic and environmental influences on BD and TUD. Therefore, we hypothesized a common underlying genetic etiology, interacting with nicotine exposure, influencing susceptibility to both BD and TUD.MethodsUsing meta-analysis, we compared TUD rates for BD patients and the general population. We identified candidate genes showing statistically significant, replicated, evidence of association with both BD and TUD. We assessed commonality among these candidate genes and hypothesized broader, multi-gene network influences on the comorbidity. Using Fisher Exact tests we tested our hypothesized genetic networks for association with the comorbidity, then compared the inferences drawn with those derived from the commonality assessment. Finally, we prioritized candidate SNPs for validation.ResultsWe estimate risk for TUD among BD patients at 2.4 times that of the general population. We found three candidate genes associated with both BD and TUD (COMT, SLC6A3, and SLC6A4) and commonality analysis suggests that these genes interact in predisposing psychiatric and substance use disorders. We identified a 69 gene network that influences neurotransmitter signaling and shows significant over-representation of genes associated with BD and TUD, as well as genes differentially expressed with exposure to tobacco smoke. Twenty four of these genes are known drug targets.ConclusionsThis work highlights novel bioinformatics resources and demonstrates the effectiveness of using an integrated bioinformatics approach to improve our understanding of complex disease etiology. We illustrate the development and testing of hypotheses for a comorbidity predisposed by both genetic and environmental influences. Consistent with our hypothesis, the selected network models multiple interacting genetic influences on comorbid BD with TUD, as well as the environmental influence of nicotine. This network nominates candidate genes for validation and drug testing, and we offer a panel of SNPs prioritized for follow-up.

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Section: Discussionmentioning

confidence: 97%

Modeling complex genetic and environmental influences on comorbid bipolar disorder with tobacco use disorder

McEachin

Saccone

et al. 2010

BMC Med Genet

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“…, Heald et al . , Stanley & Laugharne ), such as the importance of baseline screens (Waterreus & Laugharne ), and the likelihood of increasing or exacerbated physical health problems as a result of anti‐psychotics (De Hert et al . ).…”

Section: Discussionmentioning

confidence: 99%

“…The participants’ description of current screening practices as inconsistent and not systematic is very similar to what is reported in the literature (Haupt , Morrato et al . , Waterreus & Laugharne ). As represented in the first theme, screening and monitoring were considered by participants as crucial to proper diagnosis and treatment, such as clinical responsiveness to physical problems that can arise from the common use of anti‐psychotics.…”

Section: Discussionmentioning

confidence: 99%

Screening physical health? Yes! But…: nurses’ views on physical health screening in mental health care

Happell

Scott

Nankivell

et al. 2013

Journal of Clinical Nursing

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“…; Cohn & Sernyak ; De Hert et al . ; Lambert & Chapman ; NSW Department of Health ; Waterreus & Laugharne ). Data were collected from all relevant documentation held in the clinical file from 1 October 2010 to 31 March 2011, inclusive.…”

Section: Methodsmentioning

confidence: 99%

Factors influencing cardiometabolic monitoring practices in an adult community mental health service

Millar

Sands

Elsom

2014

Int J Mental Health Nurs

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People with serious mental illness are reported to live up to 25 years less than the general population. Cardiovascular disease and diabetes risk factors, as well as mental health, treatment, lifestyle, service provision, and socioeconomic factors, all contribute to this health inequity. Cardiometabolic monitoring (CMM) is one strategy used to attend to some cardiometabolic risk factors. The present study aimed to explore factors that influence decisions to undertake CMM in an Australian adult community mental health service. A CMM audit tool was designed to capture demographic, clinical, and care-provision factors. A 6-month retrospective file audit from the total population of consumers of an adult community mental health service was undertaken, where no existing CMM guidelines or practices were in place. The study findings confirmed a higher prevalence of cardiometabolic disorders in the study population compared to the general population. Complete CMM occurred in 24% of the study population (n = 94). No consumer demographic, socioeconomic, or clinical characteristics, or care-provision factors, were found to be predictors of complete CMM. The random manner in which CMM was observed to occur in the study highlights the need for standardized CMM guidelines and capacity-building strategies to improve current CMM practices.

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Screening for the metabolic syndrome in patients receiving antipsychotic treatment: a proposed algorithm

Cited by 45 publications

References 22 publications

Modeling complex genetic and environmental influences on comorbid bipolar disorder with tobacco use disorder

Modeling complex genetic and environmental influences on comorbid bipolar disorder with tobacco use disorder

Screening physical health? Yes! But…: nurses’ views on physical health screening in mental health care

Factors influencing cardiometabolic monitoring practices in an adult community mental health service

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