Hydroxyoctadecadienoic acids (HODEs) are stable oxidation products of linoleic acid, the generation of which is increased where oxidative stress is increased, such as in diabetes. In early atherosclerosis, 13-HODE is generated in macrophages by 15-lipoxygenase-1. This enhances protective mechanisms through peroxisome proliferator-activated receptor (PPAR)-g activation leading to increased clearance of lipid and lipid-laden cells from the arterial wall. In later atherosclerosis, both 9-HODE and 13-HODE are generated nonenzymatically. At this stage, early protective mechanisms are overwhelmed and pro-inflammatory effects of 9-HODE, acting through the receptor GPR132, and increased apoptosis predominate leading to a fragile, acellular plaque. Increased HODE levels thus contribute to atherosclerosis progression and the risk of clinical events such as myocardial infarction or stroke. Better understanding of the role of HODEs may lead to new pharmacologic approaches to modulate their production or action, and therefore lessen the burden of atherosclerotic disease in high-risk patients.

In this article, the authors review the current status of biomarker research in the adjuvant treatment of colon cancer, drawing on their experiences and considering future strategies for biomarker discovery in the postgenomic era.

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BackgroundTo date, our ability to accurately identify patients at high risk from suicidal behaviour, and thus to target interventions, has been fairly limited. This study examined a large pool of factors that are potentially associated with suicide risk from the comprehensive electronic medical record (EMR) and to derive a predictive model for 1–6 month risk.Methods7,399 patients undergoing suicide risk assessment were followed up for 180 days. The dataset was divided into a derivation and validation cohorts of 4,911 and 2,488 respectively. Clinicians used an 18-point checklist of known risk factors to divide patients into low, medium, or high risk. Their predictive ability was compared with a risk stratification model derived from the EMR data. The model was based on the continuation-ratio ordinal regression method coupled with lasso (which stands for least absolute shrinkage and selection operator).ResultsIn the year prior to suicide assessment, 66.8% of patients attended the emergency department (ED) and 41.8% had at least one hospital admission. Administrative and demographic data, along with information on prior self-harm episodes, as well as mental and physical health diagnoses were predictive of high-risk suicidal behaviour. Clinicians using the 18-point checklist were relatively poor in predicting patients at high-risk in 3 months (AUC 0.58, 95% CIs: 0.50 – 0.66). The model derived EMR was superior (AUC 0.79, 95% CIs: 0.72 – 0.84). At specificity of 0.72 (95% CIs: 0.70-0.73) the EMR model had sensitivity of 0.70 (95% CIs: 0.56-0.83).ConclusionPredictive models applied to data from the EMR could improve risk stratification of patients presenting with potential suicidal behaviour. The predictive factors include known risks for suicide, but also other information relating to general health and health service utilisation.