Adipose Gene Expression Prior to Weight Loss Can Differentiate and Weakly Predict Dietary Responders

BACKGROUND: The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. METHODOLOGY/PRINCIPAL FINDINGS: The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%+/-2.2%. CONCLUSION: Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition

Biomarkers in mood disorders research: developing new and improved therapeutics

Background Recently, surrogate neurobiological biomarkers that correlate with target engagement and therapeutic response have been developed and tested in early phase studies of mood disorders. Objective The identification of biomarkers could help develop personalized psychiatric treatments that may impact public health. Methods These biomarkers, which are associated with clinical response post-treatment, can be directly validated using multimodal approaches including genetic tools, proteomics/metabolomics, peripheral measures, neuroimaging, biostatistical predictors, and clinical predictors. Results To date, early phase biomarker studies have sought to identify measures that can serve as “biosignatures”, or biological patterns of clinical response. These studies have also sought to identify clinical predictors and surrogate outcomes associated with pathophysiological domains consistently described in the National Institute of Mental Health’s (NIMH) new Research Domain Criteria (RDoC). Using the N-methyl-D-aspartate (NMDA) antagonist ketamine as an example, we identified changes in several domains (clinical, cognitive, and neurophysiological) that predicted ketamine’s rapid and sustained antidepressant effects in individuals with treatment-resistant major depressive disorder (MDD) or bipolar depression. Discussion These approaches may ultimately provide clues into the neurobiology of psychiatric disorders and may have enormous impact Backon the development of novel therapeutics