PKN1 Kinase: A Key Player in Adipocyte Differentiation and Glucose Metabolism.

Adipocyte dysfunction is the driver of obesity and correlates with insulin resistance and the onset of type 2 diabetes. Protein kinase N1 (PKN1) is a serine/threonine kinase that has been shown to contribute to Glut4 translocation to the membrane and glucose transport. Here, we evaluated the role of PKN1 in glucose metabolism under insulin-resistant conditions in primary visceral adipose tissue (VAT) from 31 patients with obesity and in murine 3T3-L1 adipocytes. In addition, in vitro studies in human VAT samples and mouse adipocytes were conducted to investigate the role of PKN1 in the adipogenic maturation process and glucose homeostasis control. We show that insulin-resistant adipocytes present a decrease in PKN1 activation levels compared to nondiabetic control counterparts. We further show that PKN1 controls the adipogenesis process and glucose metabolism. PKN1-silenced adipocytes present a decrease in both differentiation process and glucose uptake, with a concomitant decrease in the expression levels of adipogenic markers, such as PPARγ, FABP4, adiponectin and CEBPα. Altogether, these results point to PKN1 as a regulator of key signaling pathways involved in adipocyte differentiation and as an emerging player of adipocyte insulin responsiveness. These findings may provide new therapeutic approaches for the management of insulin resistance in type 2 diabetes.

A Clinical-Genetic Score for Predicting Weight Loss after Bariatric Surgery: The OBEGEN Study.

Around 30% of the patients that undergo bariatric surgery (BS) do not reach an appropriate weight loss. The OBEGEN study aimed to assess the added value of genetic testing to clinical variables in predicting weight loss after BS. A multicenter, retrospective, longitudinal, and observational study including 416 patients who underwent BS was conducted (Clinical.Trials.gov- NCT02405949). 50 single nucleotide polymorphisms (SNPs) from 39 genes were examined. Receiver Operating Characteristic (ROC) curve analysis were used to calculate sensitivity and specificity. Satisfactory response to BS was defined as at nadir excess weight loss >50%. A good predictive model of response [area under ROC of 0.845 (95% CI 0.805-0.880), p < 0.001; sensitivity 90.1%, specificity 65.5%] was obtained by combining three clinical variables (age, type of surgery, presence diabetes) and nine SNPs located in ADIPOQ, MC4R, IL6, PPARG, INSIG2, CNR1, ELOVL6, PLIN1 and BDNF genes. This predictive model showed a significant higher area under ROC than the clinical score (p = 0.0186). The OBEGEN study shows the key role of combining clinical variables with genetic testing to increase the predictability of the weight loss response after BS. This finding will permit us to implement a personalized medicine which will be associated with a more cost-effective clinical practice.